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Top 100 Amazon System Design Interview Questions and Answers

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1. Explain Load Balancing in a distributed system.

Load balancing ensures even distribution of incoming traffic across multiple servers, improving system performance and fault tolerance. Amazon Elastic Load Balancer (ELB) is commonly used. Here’s a simple Python code snippet using the boto3 library:

import boto3

elb = boto3.client('elbv2')

# Create a load balancer
response = elb.create_load_balancer(
    Name='my-load-balancer',
    Subnets=['subnet-abc123', 'subnet-def456'],
    SecurityGroups=['sg-12345'],
    Scheme='internet-facing',
    Tags=[{'Key': 'Name', 'Value': 'my-lb'}]
)

print(response)

Official reference: Amazon ELB Documentation


2. How does Amazon S3 handle data consistency in a distributed system?

Amazon S3 provides strong read-after-write consistency for all objects by default. This means that once you write an object, any subsequent read will return the latest version of the object. Here’s a code snippet in Python using the boto3 library to upload an object to S3:

import boto3

s3 = boto3.client('s3')

# Upload a file to S3
s3.upload_file('my_file.txt', 'my-bucket', 'my-object-key')

print("Object uploaded successfully.")

Official reference: Amazon S3 Data Consistency Model


3. Describe how Amazon DynamoDB handles scaling in a distributed system.

DynamoDB automatically scales to handle varying workloads. You can use Provisioned Capacity or On-Demand Capacity modes. Here’s a Python code snippet using the boto3 library to create a DynamoDB table with Provisioned Capacity:

import boto3

dynamodb = boto3.client('dynamodb')

# Create a DynamoDB table with Provisioned Capacity
response = dynamodb.create_table(
    TableName='my-table',
    KeySchema=[
        {'AttributeName': 'partition_key', 'KeyType': 'HASH'},
    ],
    AttributeDefinitions=[
        {'AttributeName': 'partition_key', 'AttributeType': 'N'},
    ],
    ProvisionedThroughput={
        'ReadCapacityUnits': 5,
        'WriteCapacityUnits': 5
    }
)

print(response)

Official reference: Amazon DynamoDB Scaling


4. Explain the concept of eventual consistency in Amazon DynamoDB.

DynamoDB provides eventual consistency for reads by default. This means that if no new writes are made to a data item, eventually all accesses to that item will return the same value. Here’s a Python code snippet using the boto3 library to read an item from DynamoDB:

import boto3

dynamodb = boto3.client('dynamodb')

# Read an item from DynamoDB
response = dynamodb.get_item(
    TableName='my-table',
    Key={
        'partition_key': {'N': '12345'}
    }
)

print(response['Item'])

Official reference: DynamoDB Consistency Model


5. What is Amazon SQS and how does it facilitate message queuing in a distributed system?

Amazon Simple Queue Service (SQS) is a fully managed message queuing service. It decouples the components of a cloud application, allowing them to run independently. Here’s a Python code snippet using the boto3 library to send a message to an SQS queue:

import boto3

sqs = boto3.client('sqs')

# Send a message to an SQS queue
response = sqs.send_message(
    QueueUrl='https://sqs.us-east-1.amazonaws.com/123456789012/my-queue',
    MessageBody='Hello, world!'
)

print(response)

Official reference: Amazon SQS Documentation


6. Explain how Amazon ECS (Elastic Container Service) works in a microservices architecture.

Amazon ECS is a fully managed container orchestration service. It allows you to run and scale containerized applications on AWS. Here’s a Python code snippet using the boto3 library to create an ECS cluster:

import boto3

ecs = boto3.client('ecs')

# Create an ECS cluster
response = ecs.create_cluster(
    clusterName='my-cluster'
)

print(response)

Official reference: Amazon ECS Documentation


7. Describe the purpose of Amazon RDS (Relational Database Service) in a distributed system.

Amazon RDS is a managed relational database service that makes it easier to set up, operate, and scale a relational database in the cloud. It supports multiple database engines like MySQL, PostgreSQL, etc. Here’s a Python code snippet using the boto3 library to create an RDS instance:

import boto3

rds = boto3.client('rds')

# Create an RDS instance
response = rds.create_db_instance(
    DBInstanceIdentifier='my-db-instance',
    Engine='mysql',
    MasterUsername='my-username',
    MasterUserPassword='my-password',
    AllocatedStorage=20,
    DBInstanceClass='db.t2.micro',
    VpcSecurityGroupIds=['sg-12345'],
    AvailabilityZone='us-east-1a'
)

print(response)

Official reference: Amazon RDS Documentation


8. How does Amazon Kinesis facilitate real-time data streaming?

Amazon Kinesis is a platform for streaming data on AWS. It enables you to ingest, buffer, and process real-time data. Here’s a Python code snippet using the boto3 library to put a record into a Kinesis stream:

import boto3

kinesis = boto3.client('kinesis')

# Put a record into a Kinesis stream
response = kinesis.put_record(
    StreamName='my-stream',
    Data='Hello, Kinesis!',
    PartitionKey='12345'
)

print(response)

Official reference: Amazon Kinesis Documentation


9. Explain how Amazon Lambda works in a serverless architecture.

AWS Lambda allows you to run code without provisioning or managing servers. It’s event-driven, meaning it executes in response to events. Here’s a Python code snippet for a simple Lambda function:

def lambda_handler(event, context):
    return {
        'statusCode': 200,
        'body': 'Hello, Lambda!'
    }

Official reference: AWS Lambda Documentation


10. What is Amazon Redshift and how does it handle data warehousing?

Amazon Redshift is a fully managed data warehousing service. It’s optimized for online analytic processing (OLAP) and supports complex queries on large datasets. Here’s a Python code snippet using the boto3 library to create a Redshift cluster:

import boto3

redshift = boto3.client('redshift')

# Create a Redshift cluster
response = redshift.create_cluster(
    ClusterIdentifier='my-cluster',
    NodeType='dc2.large',
    MasterUsername='my-username',
    MasterUserPassword='my-password',
    ClusterType='single-node',
    AvailabilityZone='us-east-1a'
)

print(response)

Official reference: Amazon Redshift Documentation


11. Describe the purpose of Amazon SNS (Simple Notification Service) in a distributed system.

Amazon SNS is a fully managed notification service. It enables you to send messages or notifications to a large number of recipients through multiple delivery protocols. Here’s a Python code snippet using the boto3 library to publish a message to an SNS topic:

import boto3

sns = boto3.client('sns')

# Publish a message to an SNS topic
response = sns.publish(
    TopicArn='arn:aws:sns:us-east-1:123456789012:my-topic',
    Message='Hello, SNS!'
)

print(response)

Official reference: Amazon SNS Documentation


12. How does Amazon CloudWatch help in monitoring and managing AWS resources?

Amazon CloudWatch provides monitoring for AWS resources and applications. It collects and tracks metrics, monitors log files, and sets alarms. Here’s a Python code snippet using the boto3 library to create a CloudWatch alarm:

import boto3

cloudwatch = boto3.client('cloudwatch')

# Create a CloudWatch alarm
response = cloudwatch.put_metric_alarm(
    AlarmName='my-alarm',
    ComparisonOperator='GreaterThanThreshold',
    EvaluationPeriods=1,
    MetricName='CPUUtilization',
    Namespace='AWS/EC2',
    Period=60,
    Statistic='SampleCount',
    Threshold=90.0,
    AlarmDescription='This metric checks CPU utilization',
    ActionsEnabled=False,
    AlarmActions=[
        'arn:aws:sns:us-east-1:123456789012:my-topic',
    ],
    Unit='Percent'
)

print(response)

Official reference: Amazon CloudWatch Documentation


13. Explain the purpose of Amazon EBS (Elastic Block Store) in a distributed system.

Amazon EBS provides block-level storage volumes for use with Amazon EC2 instances. It allows you to create, attach, and detach volumes, which can be used like raw, unformatted block devices. Here’s a Python code snippet using the boto3 library to create an EBS volume:

import boto3

ec2 = boto3.client('ec2')

# Create an EBS volume
response = ec2.create_volume(
    AvailabilityZone='us-east-1a',
    Size=20,
    VolumeType='gp2'
)

print(response)

Official reference: Amazon EBS Documentation


14. What is Amazon API Gateway and how does it facilitate building and managing APIs?

Amazon API Gateway is a managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs. It acts as a front door for applications to access data, business logic, or functionality from back-end services. Here’s a Python code snippet using the boto3 library to create an API Gateway:

import boto3

apigateway = boto3.client('apigateway')

# Create an API Gateway
response = apigateway.create_rest_api(
    name='my-api',
    description='My first API',
    version='1.0'
)

print(response)

Official reference: Amazon API Gateway Documentation


15. Explain the purpose of AWS Step Functions in a distributed system.

AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services into serverless workflows. It provides a visual representation of your application’s architecture. Here’s an example state machine definition in JSON format:

{
  "StartAt": "HelloWorld",
  "States": {
    "HelloWorld": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:123456789012:function:MyLambdaFunction",
      "End": true
    }
  }
}

Official reference: AWS Step Functions Documentation


16. How does Amazon CloudFront improve the delivery of content in a distributed system?

Amazon CloudFront is a content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally. It integrates with other Amazon Web Services products. Here’s a Python code snippet to create a CloudFront distribution:

import boto3

cloudfront = boto3.client('cloudfront')

# Create a CloudFront distribution
response = cloudfront.create_distribution(
    DistributionConfig={
        'CallerReference': 'my-distribution',
        'Aliases': {
            'Quantity': 1,
            'Items': ['example.com']
        },
        'DefaultRootObject': 'index.html',
        'Origins': {
            'Quantity': 1,
            'Items': [
                {
                    'DomainName': 'my-bucket.s3.amazonaws.com',
                    'Id': 'S3-origin',
                    'S3OriginConfig': {
                        'OriginAccessIdentity': ''
                    }
                }
            ]
        }
    }
)

print(response)

Official reference: Amazon CloudFront Documentation


17. Explain the purpose of AWS Glue in a distributed system.

AWS Glue is a fully managed extract, transform, and load (ETL) service. It makes it easy to move data between data stores, transform data, and discover new data sets. Here’s an example Glue ETL job in Python:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job

args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Define ETL transformations here

job.commit()

Official reference: AWS Glue Documentation


18. How does Amazon Elasticache improve the performance of web applications in a distributed system?

Amazon ElastiCache is a fully managed in-memory data store service. It supports two popular caching engines: Redis and Memcached. Here’s a Python code snippet using redis-py, a Redis client library, to connect to a Redis cluster:

import redis

# Connect to a Redis cluster
client = redis.StrictRedis(
    host='my-redis-cluster.endpoint.cache.amazonaws.com',
    port=6379,
    decode_responses=True
)

# Set a key-value pair
client.set('my-key', 'my-value')

# Retrieve a value
value = client.get('my-key')
print(value)

Official reference: Amazon ElastiCache Documentation


19. Explain the purpose of Amazon Cognito in a distributed system.

Amazon Cognito provides authentication, authorization, and user management for your web and mobile apps. It’s a fully managed service that scales to support hundreds of millions of users. Here’s a Python code snippet using boto3 to create a Cognito User Pool:

import boto3

cognitoidentity = boto3.client('cognitoidentity')

# Create a Cognito User Pool
response = cognitoidentity.create_identity_pool(
    IdentityPoolName='my-identity-pool',
    AllowUnauthenticatedIdentities=False
)

print(response)

Official reference: Amazon Cognito Documentation


20. How does AWS IAM (Identity and Access Management) enhance security in a distributed system?

AWS IAM enables you to securely control access to AWS services and resources. It allows you to create and manage users, groups, and roles. Here’s a Python code snippet using boto3 to create an IAM user:

import boto3

iam = boto3.client('iam')

# Create an IAM user
response = iam.create_user(
    UserName='my-user'
)

print(response)

Official reference: AWS IAM Documentation


21. Explain the purpose of AWS VPC (Virtual Private Cloud) in a distributed system.

AWS VPC lets you provision a logically isolated section of the AWS Cloud where you can launch AWS resources in a virtual network. It provides control over IP addressing, subnets, and security groups. Here’s a Python code snippet using boto3 to create a VPC:

import boto3

ec2 = boto3.client('ec2')

# Create a VPC
response = ec2.create_vpc(
    CidrBlock='10.0.0.0/16'
)

print(response)

Official reference: AWS VPC Documentation


22. How does Amazon EMR (Elastic MapReduce) facilitate big data processing in a distributed system?

Amazon EMR is a cloud-based big data platform that processes large datasets using popular distributed processing frameworks such as Apache Hadoop and Spark. Here’s a Python code snippet using boto3 to create an EMR cluster:

import boto3

emr = boto3.client('emr')

# Create an EMR cluster
response = emr.run_job_flow(
    Name='my-cluster',
    Instances={
        'MasterInstanceType': 'm5.xlarge',
        'SlaveInstanceType': 'm5.xlarge',
        'InstanceCount': 2,
        'KeepJobFlowAliveWhenNoSteps': True,
    },
    Applications=[
        {'Name': 'Hadoop'},
        {'Name': 'Hive'},
        {'Name': 'Pig'},
        {'Name': 'Spark'}
    ],
    VisibleToAllUsers=True,
    JobFlowRole='EMR_EC2_DefaultRole',
    ServiceRole='EMR_DefaultRole',
    LogUri='s3://my-log-bucket/',
)

print(response)

Official reference: Amazon EMR Documentation


23. Describe the purpose of Amazon S3 Glacier in a distributed system.

Amazon S3 Glacier is a low-cost storage class for long-term data archiving. It’s designed for data that is accessed infrequently and requires long-term retention. Here’s a Python code snippet using boto3 to initiate a Glacier vault retrieval:

import boto3

glacier = boto3.client('glacier')

# Initiate a Glacier vault retrieval
response = glacier.initiate_job(
    vaultName='my-vault',
    jobParameters={
        'Type': 'archive-retrieval',
        'ArchiveId': 'my-archive-id',
        'Description': 'Retrieve my data'
    }
)

print(response)

Official reference: Amazon S3 Glacier Documentation


24. How does Amazon Route 53 enhance domain name resolution in a distributed system?

Amazon Route 53 is a scalable domain name system (DNS) web service. It’s designed to give reliable and cost-effective domain registration, DNS routing, and health checking of resources. Here’s a Python code snippet using boto3 to create a Route 53 hosted zone:

import boto3

route53 = boto3.client('route53')

# Create a Route 53 hosted zone
response = route53.create_hosted_zone(
    Name='example.com',
    CallerReference='unique-ref',
    HostedZoneConfig={
        'Comment': 'My hosted zone'
    }
)

print(response)

Official reference: Amazon Route 53 Documentation


25. Explain how AWS CloudFormation simplifies infrastructure deployment in a distributed system.

AWS CloudFormation allows you to use a template to provision and manage AWS infrastructure. It’s a way to model and set up your Amazon Web Services resources. Here’s a simple CloudFormation YAML template to create an S3 bucket:

AWSTemplateFormatVersion: '2010-09-09'
Resources:
  MyBucket:
    Type: 'AWS::S3::Bucket'
    Properties:
      BucketName: my-bucket

Official reference: AWS CloudFormation Documentation


26. What is AWS App Runner and how does it help in deploying applications in a distributed system?

AWS App Runner is a fully managed service that makes it easy for you to build, deploy, and scale containerized applications quickly. It automatically builds and deploys the application for you. Here’s a Python code snippet using boto3 to create an App Runner service:

import boto3

apprunner = boto3.client('apprunner')

# Create an App Runner service
response = apprunner.create_service(
    ServiceName='my-service',
    SourceConfiguration={
        'AutoDeploymentsEnabled': True,
        'Source': 'my-source-url'
    },
    InstanceConfiguration={
        'InstanceRoleArn': 'arn:aws:iam::123456789012:role/service-role/AppRunner-ServiceRole',
        'InstanceType': 't2.micro'
    }
)

print(response)

Official reference: AWS App Runner Documentation


27. How does AWS WAF (Web Application Firewall) enhance security in a distributed system?

AWS WAF is a web application firewall that helps protect web applications from common web exploits. It integrates with Amazon CloudFront and Application Load Balancer. Here’s a Python code snippet using boto3 to create a WAF web ACL:

import boto3

waf = boto3.client('waf')

# Create a WAF web ACL
response = waf.create_web_acl(
    Name='my-web-acl',
    MetricName='my-web-metric',
    DefaultAction={
        'Type': 'BLOCK'
    },
    Rules=[
        {
            'Action': {
                'Type': 'BLOCK'
            },
            'Priority': 1,
            'RuleId': 'AWSManagedRulesCommonRuleSet'
        }
    ]
)

print(response)

Official reference: AWS WAF Documentation


32. Explain the purpose of Amazon MQ in a distributed system.

Amazon MQ is a managed message broker service that enables you to communicate between different parts of your application. It supports multiple messaging protocols. Here’s a Python code snippet using boto3 to create an Amazon MQ broker:

import boto3

mq = boto3.client('mq')

# Create an Amazon MQ broker
response = mq.create_broker(
    BrokerName='my-broker',
    EngineType='ActiveMQ',
    EngineVersion='5.15.14',
    HostInstanceType='mq.m5.large',
    SecurityGroups=['sg-12345'],
    SubnetIds=['subnet-abc123', 'subnet-def456']
)

print(response)

Official reference: Amazon MQ Documentation


33. How does AWS DMS (Database Migration Service) help in migrating databases in a distributed system?

AWS DMS helps you migrate databases to AWS easily and securely. It supports various database engines, including MySQL, PostgreSQL, and Oracle. Here’s a Python code snippet using boto3 to create a DMS replication instance:

import boto3

dms = boto3.client('dms')

# Create a DMS replication instance
response = dms.create_replication_instance(
    ReplicationInstanceIdentifier='my-instance',
    ReplicationInstanceClass='dms.r5.large',
    AllocatedStorage=100
)

print(response)

Official reference: AWS DMS Documentation


34. What is AWS CodePipeline and how does it facilitate continuous delivery in a distributed system?

AWS CodePipeline is a fully managed continuous delivery service that helps you automate your release pipelines. It supports various source code repositories and build systems. Here’s a Python code snippet using boto3 to create a CodePipeline pipeline:

import boto3

codepipeline = boto3.client('codepipeline')

# Create a CodePipeline pipeline
response = codepipeline.create_pipeline(
    pipeline={
        'name': 'my-pipeline',
        'roleArn': 'arn:aws:iam::123456789012:role/service-role/AWS-CodePipeline-Service'
    }
)

print(response)

Official reference: AWS CodePipeline Documentation


35. Explain how AWS Fargate simplifies running containers in a distributed system.

AWS Fargate is a serverless compute engine for containers. It removes the need to manage the underlying infrastructure. Here’s a Python code snippet using boto3 to create a Fargate task definition:

import boto3

ecs = boto3.client('ecs')

# Create a Fargate task definition
response = ecs.register_task_definition(
    family='my-task-family',
    containerDefinitions=[
        {
            'name': 'my-container',
            'image': 'my-repo/my-image:latest'
        }
    ],
    networkMode='awsvpc',
    requiresCompatibilities=['FARGATE'],
    cpu='256',
    memory='0.5GB'
)

print(response)

Official reference: AWS Fargate Documentation


36. How does Amazon Neptune enhance graph database capabilities in a distributed system?

Amazon Neptune is a fully managed graph database service. It supports both Property Graph and RDF models, allowing you to build and run applications that work with highly connected datasets. Here’s a Python code snippet using boto3 to create a Neptune cluster:

import boto3

neptune = boto3.client('neptune')

# Create a Neptune cluster
response = neptune.create_db_cluster(
    DBClusterIdentifier='my-neptune-cluster',
    Engine='neptune',
    NodeType='db.r5.large',
    MasterUsername='my-username',
    MasterUserPassword='my-password'
)

print(response)

Official reference: Amazon Neptune Documentation


37. Explain how AWS Key Management Service (KMS) enhances data encryption in a distributed system.

AWS KMS makes it easy for you to create and control keys used for encryption across your applications and services. It integrates with various AWS services for seamless encryption. Here’s a Python code snippet using boto3 to create a KMS key:

import boto3

kms = boto3.client('kms')

# Create a KMS key
response = kms.create_key(
    Policy='{"Version": "2012-10-17", "Statement": [{"Effect": "Allow", "Principal": {"Service": "dynamodb.amazonaws.com"}, "Action": "kms:Encrypt", "Resource": "*"}]}',
    Description='My KMS key'
)

print(response)

Official reference: AWS KMS Documentation


38. What is AWS Service Catalog and how does it help in managing services in a distributed system?

AWS Service Catalog enables organizations to create and manage catalogs of IT services that are approved for use on AWS. It helps you achieve consistent governance and meet compliance requirements. Here’s a Python code snippet using boto3 to create a Service Catalog product:

import boto3

servicecatalog = boto3.client('servicecatalog')

# Create a Service Catalog product
response = servicecatalog.create_product(
    Name='my-product',
    Owner='my-team',
    Description='My first product',
    Distributor='my-distributor',
    SupportDescription='Support for my product'
)

print(response)

Official reference: AWS Service Catalog Documentation


39. How does AWS OpsWorks facilitate application deployment and management in a distributed system?

AWS OpsWorks is a configuration management service that helps you automate operational tasks like code deployment, software configurations, and server management. Here’s a Python code snippet using boto3 to create an OpsWorks stack:

import boto3

opsworks = boto3.client('opsworks')

# Create an OpsWorks stack
response = opsworks.create_stack(
    Name='my-stack',
    Region='us-east-1',
    VpcId='vpc-12345',
    DefaultInstanceProfileArn='arn:aws:iam::123456789012:instance-profile/my-profile',
    ServiceRoleArn='arn:aws:iam::123456789012:role/my-role'
)

print(response)

Official reference: AWS OpsWorks Documentation


40. Explain the purpose of AWS Data Pipeline in a distributed system.

AWS Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premises data sources. Here’s a Python code snippet using boto3 to create a Data Pipeline pipeline:

import boto3

datapipeline = boto3.client('datapipeline')

# Create a Data Pipeline pipeline
response = datapipeline.create_pipeline(
    name='my-pipeline',
    uniqueId='my-pipeline-123',
    description='My first pipeline',
    tags=[
        {
            'key': 'Environment',
            'value': 'Production'
        }
    ]
)

print(response)

Official reference: AWS Data Pipeline Documentation


41. How does AWS Transfer Family help in securely transferring files in a distributed system?

AWS Transfer Family is a fully managed service that enables the transfer of files over SFTP, FTPS, or FTP directly into and out of Amazon S3. Here’s a Python code snippet using boto3 to create an SFTP server using AWS Transfer Family:

import boto3

transfer = boto3.client('transfer')

# Create an SFTP server
response = transfer.create_server(
    IdentityProviderType='SERVICE_MANAGED',
    EndpointType='PUBLIC'
)

print(response)

Official reference: AWS Transfer Family Documentation


42. Explain the purpose of Amazon Elastic Inference in a distributed system.

Amazon Elastic Inference allows you to attach just the right amount of GPU-powered inference acceleration to your endpoint. It helps reduce the cost of running deep learning inference. Here’s a Python code snippet using boto3 to configure Elastic Inference for an EC2 instance:

import boto3

ec2 = boto3.client('ec2')

# Configure Elastic Inference for an EC2 instance
response = ec2.modify_instance_attribute(
    InstanceId='i-1234567890abcdef0',
    ElasticInferenceAccelerators=[
        {
            'Type': 'eia2.medium'
        }
    ]
)

print(response)

Official reference: Amazon Elastic Inference Documentation


43. What is AWS Budgets and how does it help in managing costs in a distributed system?

AWS Budgets is a service that allows you to set custom cost and usage budgets for your AWS accounts. It provides you with the ability to track your AWS spending. Here’s a Python code snippet using boto3 to create an AWS Budget:

import boto3

budgets = boto3.client('budgets')

# Create an AWS Budget
response = budgets.create_budget(
    AccountId='123456789012',
    Budget={
        'BudgetName': 'my-budget',
        'BudgetLimit': {
            'Amount': '100.0',
            'Unit': 'USD'
        },
        'TimeUnit': 'MONTHLY',
        'BudgetType': 'COST'
    }
)

print(response)

Official reference: AWS Budgets Documentation


44. How does AWS Snowball facilitate large-scale data transfers in a distributed system?

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of AWS. It helps overcome challenges of high-speed internet. Here’s a Python code snippet using boto3 to create a Snowball job:

import boto3

snowball = boto3.client('snowball')

# Create a Snowball job
response = snowball.create_job(
    JobType='IMPORT',
    Resources={
        'S3Resources': [
            {
                'BucketArn': 'arn:aws:s3:::my-bucket'
            }
        ]
    },
    Description='My Snowball job'
)

print(response)

Official reference: AWS Snowball Documentation


45. Explain the purpose of AWS Glue DataBrew in a distributed system.

AWS Glue DataBrew is a visual data preparation tool that makes it easy to clean and normalize data. It helps data engineers and analysts discover, clean, and transform data. Here’s a Python code snippet using boto3 to create a Glue DataBrew project:

import boto3

gluedatabrew = boto3.client('databrew')

# Create a Glue DataBrew project
response = gluedatabrew.create_project(
    DatasetName='my-dataset',
    Name='my-project',
    RecipeName='my-recipe'
)

print(response)

Official reference: AWS Glue DataBrew Documentation


46. What is AWS Deep Learning AMI and how does it support deep learning in a distributed system?

AWS Deep Learning AMI is a set of pre-configured virtual machines that allow you to quickly launch Amazon EC2 instances for deep learning. It comes with popular deep learning frameworks pre-installed. Here’s a Python code snippet using boto3 to launch an EC2 instance with a Deep Learning AMI:

import boto3

ec2 = boto3.client('ec2')

# Launch an EC2 instance with Deep Learning AMI
response = ec2.run_instances(
    ImageId='ami-12345678',
    InstanceType='p3.2xlarge',
    MinCount=1,
    MaxCount=1
)

print(response)

Official reference: AWS Deep Learning AMI Documentation


47. How does Amazon Kinesis Data Streams facilitate real-time data streaming in a distributed system?

Amazon Kinesis Data Streams allows you to build custom applications that process or analyze streaming data for specialized needs. It supports real-time data ingestion and processing. Here’s a Python code snippet using boto3 to create a Kinesis Data Stream:

import boto3

kinesis = boto3.client('kinesis')

# Create a Kinesis Data Stream
response = kinesis.create_stream(
    StreamName='my-stream',
    ShardCount=1
)

print(response)

Official reference: Amazon Kinesis Data Streams Documentation


48. Explain the purpose of AWS Elemental MediaConvert in a distributed system.

AWS Elemental MediaConvert is a file-based video processing service that allows you to easily and reliably transcode on-demand content for broadcast and multiscreen delivery. Here’s a Python code snippet using boto3 to create a MediaConvert job:

import boto3

mediaconvert = boto3.client('mediaconvert')

# Create a MediaConvert job
response = mediaconvert.create_job(
    Role='arn:aws:iam::123456789012:role/MediaConvertRole',
    Settings={
        'OutputGroups': [
            {
                'Name': 'File Group',
                'Outputs': [
                    {
                        'ContainerSettings': {
                            'Container': 'MP4'
                        },
                        'Extension': 'mp4',
                        'VideoDescription': {
                            'Width': 1920,
                            'Height': 1080
                        }
                    }
                ]
            }
        ]
    },
    Queue='arn:aws:mediaconvert:us-west-2:123456789012:queues/Default'
)

print(response)

Official reference: AWS Elemental MediaConvert Documentation


49. How does AWS Personalize enhance recommendation systems in a distributed system?

AWS Personalize is a fully managed service that makes it easy to create and manage personalized recommendations for content. It uses machine learning algorithms. Here’s a Python code snippet using boto3 to create a Personalize solution:

import boto3

personalize = boto3.client('personalize')

# Create a Personalize solution
response = personalize.create_solution(
    name='my-solution',
    datasetGroupArn='arn:aws:personalize:us-west-2:123456789012:dataset-group/my-dataset-group',
    recipeArn='arn:aws:personalize:::recipe/aws-hrnn'
)

print(response)

Official reference: AWS Personalize Documentation


50. What is AWS Sumerian and how does it facilitate building AR/VR applications in a distributed system?

AWS Sumerian is a set of tools for creating high-quality virtual reality (VR) and augmented reality (AR) applications. It provides a web-based editor. Here’s a Python code snippet using boto3 to create a Sumerian scene:

import boto3

sumerian = boto3.client('sumerian')

# Create a Sumerian scene
response = sumerian.create_scene(
    name='my-scene',
    description='My first scene',
    templateId='urn:amazon:sumerian:templates/EmptySample/1'
)

print(response)

Official reference: AWS Sumerian Documentation


51. Explain how AWS Lake Formation simplifies building, securing, and managing data lakes in a distributed system.

AWS Lake Formation is a service that makes it easy to set up, secure, and manage a data lake. It streamlines the process of building data lakes. Here’s a Python code snippet using boto3 to create a Lake Formation database:

import boto3

lakeformation = boto3.client('lakeformation')

# Create a Lake Formation database
response = lakeformation.create_database(
    CatalogId='123456789012',
    DatabaseInput={
        'Name': 'my-database',
        'Description': 'My first database',
        'LocationUri': 's3://my-bucket/'
    }
)

print(response)

Official reference: AWS Lake Formation Documentation


52. How does AWS AppFlow facilitate secure data transfer between AWS services and SaaS applications in a distributed system?

AWS AppFlow is a fully managed integration service that allows you to securely transfer data between AWS services and SaaS applications. It supports various connectors. Here’s a Python code snippet using boto3 to create an AppFlow flow:

import boto3

appflow = boto3.client('appflow')

# Create an AppFlow flow
response = appflow.create_flow(
    flowName='my-flow',
    sourceFlowConfig={
        'connectorType': 'Salesforce',
        'sourceConnectorProperties': {
            'Salesforce': {
                'object': 'Account'
            }
        }
    },
    destinationFlowConfig={
        'connectorType': 'S3',
        'destinationConnectorProperties': {
            'S3': {
                'bucketName': 'my-s3-bucket',
                'bucketPrefix': 'my-prefix'
            }
        }
    }
)

print(response)

Official reference: AWS AppFlow Documentation


53. Explain the purpose of Amazon Managed Service for Prometheus in a distributed system.

Amazon Managed Service for Prometheus is a fully managed service that makes it easy to monitor containerized applications. It provides a highly available and secure environment. Here’s a Python code snippet using boto3 to create a Managed Service for Prometheus workspace:

import boto3

prometheus = boto3.client('prometheus')

# Create a Managed Service for Prometheus workspace
response = prometheus.create_workspace(
    WorkspaceName='my-workspace',
    PrometheusEndpoint='https://prometheus.mydomain.com',
    DataSourceEndpoint='https://datasource.mydomain.com'
)

print(response)

Official reference: Amazon Managed Service for Prometheus Documentation


54. What is AWS Cloud9 and how does it facilitate cloud-based integrated development environments in a distributed system?

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug code from a browser. It supports various programming languages. Here’s a Python code snippet using boto3 to create a Cloud9 environment:

import boto3

cloud9 = boto3.client('cloud9')

# Create a Cloud9 environment
response = cloud9.create_environment_ec2(
    name='my-environment',
    instanceType='t2.micro',
    automaticStopTimeMinutes=60
)

print(response)

Official reference: AWS Cloud9 Documentation


55. How does AWS RoboMaker simplify the development, testing, and deployment of robotics applications in a distributed system?

AWS RoboMaker is a service that makes it easy to develop, test, and deploy intelligent robotics applications at scale. It provides tools for simulation and development. Here’s a Python code snippet using boto3 to create a RoboMaker simulation job:

import boto3

robomaker = boto3.client('robomaker')

# Create a RoboMaker simulation job
response = robomaker.create_simulation_job(
    jobName='my-simulation-job',
    simulationApplications=[
        {
            'application': 'arn:aws:robomaker:us-west-2:123456789012:simulation-application/my-app/1',
            'launchConfig': {
                'packageName': 'my-package',
                'launchFile': 'my-launch-file.launch'
            }
        }
    ]
)

print(response)

Official reference: AWS RoboMaker Documentation


60. How does AWS CloudFormation facilitate the creation and management of AWS resources in a distributed system?

AWS CloudFormation allows you to define your AWS infrastructure as code in templates. It helps automate resource provisioning and management. Here’s a Python code snippet using boto3 to create a CloudFormation stack:

import boto3

cloudformation = boto3.client('cloudformation')

# Create a CloudFormation stack
response = cloudformation.create_stack(
    StackName='my-stack',
    TemplateURL='https://s3.amazonaws.com/my-bucket/my-template.yml'
)

print(response)

Official reference: AWS CloudFormation Documentation


61. Explain the purpose of AWS Cost Explorer in a distributed system.

AWS Cost Explorer is a free tool that helps you visualize, understand, and manage your AWS spending. It provides insights into your AWS costs. Here’s a Python code snippet using boto3 to get cost and usage data:

import boto3

ce = boto3.client('ce')

# Get cost and usage data
response = ce.get_cost_and_usage(
    TimePeriod={
        'Start': '2023-09-01',
        'End': '2023-09-07'
    },
    Granularity='DAILY',
    Metrics=['BlendedCost']
)

print(response)

Official reference: AWS Cost Explorer Documentation


62. What is AWS AppConfig and how does it help in managing application configurations in a distributed system?

AWS AppConfig helps you deploy application configurations in a managed and controlled way. It enables you to separate code deployment from configuration changes. Here’s a Python code snippet using boto3 to create an AppConfig application:

import boto3

appconfig = boto3.client('appconfig')

# Create an AppConfig application
response = appconfig.create_application(
    Name='my-app',
    Description='My AppConfig application',
    Tags={
        'Environment': 'Production'
    }
)

print(response)

Official reference: AWS AppConfig Documentation


63. How does Amazon Pinpoint facilitate targeted messaging and notifications in a distributed system?

Amazon Pinpoint is a fully managed, multi-channel engagement service. It helps you send targeted messages to your customers. Here’s a Python code snippet using boto3 to send a message through Pinpoint:

import boto3

pinpoint = boto3.client('pinpoint')

# Send a message through Amazon Pinpoint
response = pinpoint.send_messages(
    ApplicationId='12345678901234567890',
    MessageRequest={
        'Addresses': {
            'destination@email.com': {'ChannelType': 'EMAIL'}
        },
        'MessageConfiguration': {
            'EmailMessage': {
                'SimpleEmail': {
                    'Subject': {'Data': 'Subject'},
                    'HtmlPart': {'Data': '<html><body>Body</body></html>'}
                }
            }
        }
    }
)

print(response)

Official reference: Amazon Pinpoint Documentation


64. Explain the purpose of AWS Step Functions in a distributed system.

AWS Step Functions is a serverless orchestration service that lets you coordinate distributed applications and microservices using visual workflows. It helps you build and coordinate applications. Here’s a Python code snippet using boto3 to create a Step Functions state machine:

import boto3

stepfunctions = boto3.client('stepfunctions')

# Create a Step Functions state machine
response = stepfunctions.create_state_machine(
    name='my-state-machine',
    definition='''{
        "Comment": "My State Machine",
        "StartAt": "HelloWorld",
        "States": {
            "HelloWorld": {
                "Type": "Pass",
                "Result": "Hello, World!",
                "End": true
            }
        }
    }'''
)

print(response)

Official reference: AWS Step Functions Documentation


65. What is AWS Secrets Manager and how does it facilitate the management of sensitive information in a distributed system?

AWS Secrets Manager helps you protect sensitive information like database credentials, API keys, and other secrets. It enables you to rotate, manage, and retrieve these secrets. Here’s a Python code snippet using boto3 to create a Secrets Manager secret:

import boto3

secretsmanager = boto3.client('secretsmanager')

# Create a Secrets Manager secret
response = secretsmanager.create_secret(
    Name='my-secret',
    SecretString='{"username": "myuser", "password": "mypassword"}'
)

print(response)

Official reference: AWS Secrets Manager Documentation


66. How does AWS Cloud Map simplify service discovery in a distributed system?

AWS Cloud Map is a fully managed service that allows you to easily create, maintain, and discover services. It provides a way to register and resolve service instances. Here’s a Python code snippet using boto3 to create a Cloud Map namespace:

import boto3

cloudmap = boto3.client('servicediscovery')

# Create a Cloud Map namespace
response = cloudmap.create_namespace(
    Name='my-namespace',
    CreatorRequestId='12345',
    Description='My namespace'
)

print(response)

Official reference: AWS Cloud Map Documentation


67. Explain the purpose of AWS Transit Gateway in a distributed system.

AWS Transit Gateway is a service that enables you to connect your Amazon VPCs and on-premises networks to a single gateway. It simplifies network architecture. Here’s a Python code snippet using boto3 to create a Transit Gateway:

import boto3

ec2 = boto3.client('ec2')

# Create a Transit Gateway
response = ec2.create_transit_gateway(
    Description='My Transit Gateway',
    Options={
        'AmazonSideAsn': 64512,
        'AutoAcceptSharedAttachments': 'enable'
    }
)

print(response)

Official reference: AWS Transit Gateway Documentation


68. How does Amazon EKS (Elastic Kubernetes Service) simplify the deployment, management, and scaling of containerized applications in a distributed system?

Amazon EKS is a managed Kubernetes service that simplifies the deployment, management, and scaling of containerized applications using Kubernetes. It provides a highly available and secure environment. Here’s a Python code snippet using boto3 to create an EKS cluster:

import boto3

eks = boto3.client('eks')

# Create an EKS cluster
response = eks.create_cluster(
    name='my-cluster',
    roleArn='arn:aws:iam::123456789012:role/eks-role',
    resourcesVpcConfig={
        'subnetIds': [
            'subnet-12345678',
            'subnet-23456789'
        ]
    }
)

print(response)

Official reference: Amazon EKS Documentation


69. Explain the purpose of AWS IoT Core in a distributed system.

AWS IoT Core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices. It enables IoT applications. Here’s a Python code snippet using boto3 to publish a message to an IoT topic:

import boto3

iot = boto3.client('iot-data')

# Publish a message to an IoT topic
response = iot.publish(
    topic='my/topic',
    qos=1,
    payload='Hello, IoT!'
)

print(response)

Official reference: AWS IoT Core Documentation


70. What is AWS App Runner and how does it simplify building and deploying containerized applications in a distributed system?

AWS App Runner is a fully managed service that makes it easy for you to build, deploy, and scale containerized applications quickly. It abstracts away the underlying infrastructure. Here’s a Python code snippet using boto3 to create an App Runner service:

import boto3

apprunner = boto3.client('apprunner')

# Create an App Runner service
response = apprunner.create_service(
    ServiceName='my-service',
    SourceConfiguration={
        'ImageRepository': {
            'imageIdentifier': 'public.ecr.aws/nginx/nginx:latest'
        }
    }
)

print(response)

Official reference: AWS App Runner Documentation


71. How does AWS Database Migration Service (DMS) facilitate the migration of databases to AWS in a distributed system?

AWS DMS helps you migrate databases to AWS quickly and securely. It supports homogenous and heterogenous migrations. Here’s a Python code snippet using boto3 to create a DMS migration task:

import boto3

dms = boto3.client('dms')

# Create a DMS migration task
response = dms.create_replication_task(
    MigrationType='full-load',
    SourceEndpointArn='arn:aws:dms:us-west-2:123456789012:endpoint/source-endpoint',
    TargetEndpointArn='arn:aws:dms:us-west-2:123456789012:endpoint/target-endpoint',
    MigrationTaskName='my-migration-task',
    TableMappings='...JSON string...'
)

print(response)

Official reference: AWS Database Migration Service Documentation


72. Explain the purpose of AWS Glue in a distributed system.

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for you to prepare and load your data for analytics. It provides a serverless data integration solution. Here’s a Python code snippet using boto3 to create a Glue job:

import boto3

glue = boto3.client('glue')

# Create a Glue job
response = glue.create_job(
    Name='my-job',
    Role='arn:aws:iam::123456789012:role/GlueRole',
    Command={
        'Name': 'glueetl',
        'ScriptLocation': 's3://my-bucket/glue-script.py'
    },
    DefaultArguments={
        '--job-language': 'python',
        '--job-bookmark-option': 'job-bookmark-enable'
    }
)

print(response)

Official reference: AWS Glue Documentation


73. How does AWS Lambda simplify serverless computing in a distributed system?

AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. It automatically scales and executes code in response to triggers. Here’s a Python code snippet using boto3 to create a Lambda function:

import boto3

lambda_client = boto3.client('lambda')

# Create a Lambda function
response = lambda_client.create_function(
    FunctionName='my-function',
    Runtime='python3.8',
    Role='arn:aws:iam::123456789012:role/lambda-role',
    Handler='lambda_function.handler',
    Code={
        'S3Bucket': 'my-bucket',
        'S3Key': 'lambda-code.zip'
    }
)

print(response)

Official reference: AWS Lambda Documentation


74. What is AWS Key Management Service (KMS) and how does it facilitate the management of encryption keys in a distributed system?

AWS KMS is a fully managed encryption service that allows you to create and control the encryption keys used to encrypt your data. It integrates with other AWS services. Here’s a Python code snippet using boto3 to create a KMS key:

import boto3

kms = boto3.client('kms')

# Create a KMS key
response = kms.create_key(
    Policy='...',
    Description='My KMS key',
    KeyUsage='ENCRYPT_DECRYPT'
)

print(response)

Official reference: AWS KMS Documentation


75. How does Amazon Cognito facilitate user authentication and authorization in a distributed system?

Amazon Cognito is a fully managed service that makes it easy to add user sign-up, sign-in, and access control to your web and mobile apps. It provides a secure user directory. Here’s a Python code snippet using boto3 to create a Cognito user pool:

import boto3

cognitoidentity = boto3.client('cognito-identity')

# Create a Cognito user pool
response = cognitoidentity.create_identity_pool(
    IdentityPoolName='my-identity-pool',
    AllowUnauthenticatedIdentities=False,
    SupportedLoginProviders={
        'cognito-idp.us-west-2.amazonaws.com/us-west-2_123456789': 'my-user-pool-id'
    }
)

print(response)

Official reference: Amazon Cognito Documentation


76. Explain the purpose of AWS Elemental MediaConvert in a distributed system.

AWS Elemental MediaConvert is a file-based video processing service that provides scalable video transcoding. It helps you convert media files for different devices and formats. Here’s a Python code snippet using boto3 to create a MediaConvert job:

import boto3

mediaconvert = boto3.client('mediaconvert')

# Create a MediaConvert job
response = mediaconvert.create_job(
    Role='arn:aws:iam::123456789012:role/MediaConvertRole',
    Settings={
        'OutputGroups': [
            {
                'Name': 'File Group',
                'Outputs': [
                    {
                        'ContainerSettings': {
                            'Container': 'MP4'
                        },
                        'VideoDescription': {
                            'ScalingBehavior': 'DEFAULT',
                            'Width': 1920,
                            'Height': 1080
                        }
                    }
                ]
            }
        ],
        'Inputs': [
            {
                'FileInput': 's3://my-bucket/my-input-file.mp4',
                'InputClippings': [
                    {
                        'StartTimecode': '00:00:00:00'
                    }
                ]
            }
        ]
    }
)

print(response)

Official reference: AWS Elemental MediaConvert Documentation


77. How does AWS DataSync simplify data transfer and migration between on-premises storage and AWS services in a distributed system?

AWS DataSync is a fully managed service that makes it easy to move large amounts of data between on-premises storage and AWS services. It provides fast and secure data transfer. Here’s a Python code snippet using boto3 to create a DataSync task:

import boto3

datasync = boto3.client('datasync')

# Create a DataSync task
response = datasync.create_task(
    SourceLocationArn='arn:aws:datasync:us-west-2:123456789012:location/location-id',
    DestinationLocationArn='arn:aws:datasync:us-west-2:123456789012:location/location-id',
    CloudWatchLogGroupArn='arn:aws:logs:us-west-2:123456789012:log-group:log-group-name',
    Name='my-datasync-task',
    Options={
        'VerifyMode': 'POINT_IN_TIME_CONSISTENT'
    }
)

print(response)

Official reference: AWS DataSync Documentation


78. What is AWS Step Functions Express Workflows and how does it enhance workflow automation in a distributed system?

AWS Step Functions Express Workflows is a low-latency, high-throughput workflow type for Lambda functions. It’s designed for high-speed applications. Here’s a Python code snippet using boto3 to create an Express Workflow:

import boto3

stepfunctions = boto3.client('stepfunctions')

# Create an Express Workflow
response = stepfunctions.create_state_machine(
    name='my-express-workflow',
    definition='''{
        "Comment": "My Express Workflow",
        "StartAt": "HelloWorld",
        "States": {
            "HelloWorld": {
                "Type": "Task",
                "Resource": "arn:aws:lambda:us-west-2:123456789012:function:my-lambda-function",
                "End": true
            }
        }
    }'''
)

print(response)

Official reference: AWS Step Functions Express Workflows Documentation


79. How does AWS Elastic Beanstalk simplify the deployment and management of applications in a distributed system?

AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services. It abstracts away the underlying infrastructure. Here’s a Python code snippet using boto3 to create an Elastic Beanstalk application:

import boto3

elasticbeanstalk = boto3.client('elasticbeanstalk')

# Create an Elastic Beanstalk application
response = elasticbeanstalk.create_application(
    ApplicationName='my-app',
    Description='My Elastic Beanstalk application'
)

print(response)

Official reference: AWS Elastic Beanstalk Documentation


80. Explain the purpose of AWS AppSync in a distributed system.

AWS AppSync is a managed serverless GraphQL service that simplifies application development by letting you create a flexible API to securely access, manipulate, and combine data from multiple sources. Here’s a Python code snippet using boto3 to create an AppSync API:

import boto3

appsync = boto3.client('appsync')

# Create an AppSync API
response = appsync.create_graphql_api(
    name='my-api',
    authenticationType='API_KEY'
)

print(response)

Official reference: AWS AppSync Documentation


81. How does AWS Step Functions Input and Output processing enhance workflow automation in a distributed system?

AWS Step Functions Input and Output processing allows you to modify and transform the data as it passes from one state to another in a workflow. It enables data manipulation. Here’s a Python code snippet using boto3 to create a Step Functions state machine with Input and Output processing:

import boto3

stepfunctions = boto3.client('stepfunctions')

# Create a Step Functions state machine with Input and Output processing
response = stepfunctions.create_state_machine(
    name='my-state-machine',
    definition='''{
        "Comment": "My State Machine",
        "StartAt": "TransformInput",
        "States": {
            "TransformInput": {
                "Type": "Pass",
                "Result": {
                    "ModifiedInput": "Hello, World!"
                },
                "ResultPath": "$.Input",
                "Next": "ProcessData"
            },
            "ProcessData": {
                "Type": "Task",
                "Resource": "arn:aws:lambda:us-west-2:123456789012:function:my-lambda-function",
                "InputPath": "$.Input.ModifiedInput",
                "End": true
            }
        }
    }'''
)

print(response)

Official reference: AWS Step Functions Input and Output Processing Documentation


82. What is AWS Fargate and how does it simplify the deployment of containerized applications in a distributed system?

AWS Fargate is a serverless compute engine for containers that works with both Amazon ECS and EKS. It removes the need to manage the underlying infrastructure. Here’s a Python code snippet using boto3 to create an ECS Fargate task definition:

import boto3

ecs = boto3.client('ecs')

# Create an ECS Fargate task definition
response = ecs.register_task_definition(
    family='my-task-family',
    networkMode='awsvpc',
    containerDefinitions=[
        {
            'name': 'my-container',
            'image': 'my-image',
            'memory': 512,
            'cpu': 256,
            'essential': True,
            'portMappings': [
                {
                    'containerPort': 80,
                    'hostPort': 80
                }
            ]
        }
    ]
)

print(response)

Official reference: AWS Fargate Documentation


83. How does AWS Cloud Development Kit (CDK) simplify the process of defining cloud infrastructure in a distributed system?

AWS CDK is an open-source software development framework to define cloud infrastructure in code and provision it through AWS CloudFormation. It makes infrastructure as code more approachable. Here’s a Python code snippet using CDK to define an S3 bucket:

from aws_cdk import core
from aws_cdk import aws_s3 as s3

class MyStack(core.Stack):
    def __init__(self, scope: core.Construct, id: str, **kwargs) -> None:
        super().__init__(scope, id, **kwargs)

        # Define an S3 bucket
        s3.Bucket(self, 
            "MyBucket",
            versioned=True
        )

Official reference: AWS CDK Documentation


84. Explain the purpose of Amazon DocumentDB (with MongoDB compatibility) in a distributed system.

Amazon DocumentDB is a fully managed, highly available, and scalable document database service that’s compatible with MongoDB. It’s designed for applications with JSON-like documents. Here’s a Python code snippet using boto3 to create a DocumentDB cluster:

import boto3

docdb = boto3.client('docdb')

# Create a DocumentDB cluster
response = docdb.create_db_cluster(
    DBClusterIdentifier='my-cluster',
    MasterUsername='myuser',
    MasterUserPassword='mypassword',
    Engine='docdb',
    AvailabilityZones=[
        'us-west-2a',
        'us-west-2b'
    ]
)

print(response)

Official reference: Amazon DocumentDB Documentation


85. How does AWS Data Pipeline facilitate the orchestration and automation of data-driven workflows in a distributed system?

AWS Data Pipeline is a web service for orchestrating and automating the movement and transformation of data across the AWS ecosystem. It simplifies data-driven workflows. Here’s a Python code snippet using boto3 to create a Data Pipeline:

import boto3

datapipeline = boto3.client('datapipeline')

# Create a Data Pipeline
response = datapipeline.create_pipeline(
    name='my-data-pipeline',
    uniqueId='my-data-pipeline-id',
    description='My Data Pipeline'
)

print(response)

Official reference: AWS Data Pipeline Documentation


86. What is Amazon Elastic MapReduce (EMR) and how does it simplify the processing of large datasets in a distributed system?

Amazon EMR is a cloud-native big data platform that simplifies the processing of large datasets using popular frameworks like Apache Hadoop and Spark. It’s designed for scalability. Here’s a Python code snippet using boto3 to create an EMR cluster:

import boto3

emr = boto3.client('emr')

# Create an EMR cluster
response = emr.run_job_flow(
    Name='my-emr-cluster',
    ReleaseLabel='emr-6.4.0',
    Instances={
        'InstanceGroups': [
            {
                'Name': 'Master',
                'InstanceRole': 'MASTER',
                'InstanceType': 'm5.xlarge',
                'InstanceCount': 1
            }
        ],
        'Ec2KeyName': 'my-key-pair',
        'KeepJobFlowAliveWhenNoSteps': True,
        'TerminationProtected': False
    },
    Applications=[
        {'Name': 'Hadoop'},
        {'Name': 'Spark'}
    ]
)

print(response)

Official reference: Amazon EMR Documentation


87. How does AWS CodePipeline simplify the continuous integration and continuous delivery (CI/CD) process in a distributed system?

AWS CodePipeline is a continuous integration and continuous delivery (CI/CD) service that automates the build, test, and deployment phases of your release process. It streamlines software release workflows. Here’s a Python code snippet using boto3 to create a CodePipeline:

import boto3

codepipeline = boto3.client('codepipeline')

# Create a CodePipeline
response = codepipeline.create_pipeline(
    pipeline={
        'name': 'my-pipeline',
        'roleArn': 'arn:aws:iam::123456789012:role/service-role/CodePipelineRole',
        'artifactStore': {
            'type': 'S3',
            'location': 'my-pipeline-artifacts'
        },
        'stages': [
            {
                'name': 'Source',
                'actions': [
                    {
                        'name': 'SourceAction',
                        'actionTypeId': {
                            'category': 'Source',
                            'owner': 'AWS',
                            'provider': 'CodeCommit',
                            'version': '1'
                        },
                        'configuration': {
                            'RepositoryName': 'my-repo',
                            'BranchName': 'main'
                        },
                        'outputArtifacts': [
                            {
                                'name': 'SourceOutput'
                            }
                        ],
                        'runOrder': 1
                    }
                ]
            }
        ]
    }
)

print(response)

Official reference: AWS CodePipeline Documentation


88. Explain the purpose of Amazon Kinesis Data Streams in a distributed system.

Amazon Kinesis Data Streams is a real-time data streaming service that allows you to collect and process large streams of data records in real time. It’s used for building custom applications. Here’s a Python code snippet using boto3 to create a Kinesis Data Stream:

import boto3

kinesis = boto3.client('kinesis')

# Create a Kinesis Data Stream
response = kinesis.create_stream(
    StreamName='my-stream',
    ShardCount=1
)

print(response)

Official reference: Amazon Kinesis Data Streams Documentation


89. How does AWS Certificate Manager (ACM) simplify the process of managing SSL/TLS certificates in a distributed system?

AWS Certificate Manager (ACM) is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources. It simplifies certificate management. Here’s a Python code snippet using boto3 to request a certificate:

import boto3

acm = boto3.client('acm')

# Request a public SSL certificate
response = acm.request_certificate(
    DomainName='example.com',
    ValidationMethod='DNS',
    SubjectAlternativeNames=[
        'www.example.com'
    ]
)

print(response)

Official reference: AWS Certificate Manager Documentation


90. What is Amazon EventBridge and how does it facilitate event-driven architecture in a distributed system?

Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. It enables event-driven architecture. Here’s a Python code snippet using boto3 to create an EventBridge rule:

import boto3

events = boto3.client('events')

# Create an EventBridge rule
response = events.put_rule(
    Name='my-rule',
    EventPattern='...',
    State='ENABLED'
)

print(response)

Official reference: Amazon EventBridge Documentation


91. How does Amazon Managed Service for Prometheus (AMP) simplify the monitoring of containerized applications in a distributed system?

Amazon Managed Service for Prometheus (AMP) is a fully managed Prometheus-compatible monitoring service that makes it easy to monitor containerized applications. It automatically scales with your workloads. Here’s a Python code snippet using boto3 to create an AMP workspace:

import boto3

amp = boto3.client('amp')

# Create an AMP workspace
response = amp.create_workspace(
    Alias='my-workspace',
    Region='us-west-2'
)

print(response)

Official reference: Amazon Managed Service for Prometheus Documentation


92. Explain the purpose of AWS Elastic Load Balancer (ELB) in a distributed system.

AWS Elastic Load Balancer automatically distributes incoming application traffic across multiple targets, such as EC2 instances, containers, and IP addresses, in one or more Availability Zones. It improves the availability and fault tolerance of your application. Here’s a Python code snippet using boto3 to create an ELB:

import boto3

elbv2 = boto3.client('elbv2')

# Create an Application Load Balancer
response = elbv2.create_load_balancer(
    Name='my-load-balancer',
    Subnets=[
        'subnet-0123456789abcdef0',
        'subnet-0123456789abcdef1'
    ],
    Scheme='internet-facing',
    LoadBalancerAttributes=[
        {
            'Key': 'idle_timeout.timeout_seconds',
            'Value': '60'
        }
    ]
)

print(response)

Official reference: AWS Elastic Load Balancer Documentation


93. How does AWS App Runner simplify the process of building, deploying, and scaling containerized applications in a distributed system?

AWS App Runner is a fully managed service that makes it easy to build, deploy, and scale containerized applications quickly. It handles all the operational aspects for you. Here’s a Python code snippet using boto3 to create an App Runner service:

import boto3

apprunner = boto3.client('apprunner')

# Create an App Runner service
response = apprunner.create_service(
    ServiceName='my-service',
    SourceConfiguration={
        'AutoDeploymentsEnabled': True,
        'AuthenticationConfiguration': {
            'ConnectionArn': 'arn:aws:app-runner:us-west-2:123456789012:connection/connection-id'
        },
        'CodeRepository': {
            'RepositoryUrl': 'https://github.com/my/repo.git',
            'SourceCodeVersion': {
                'Type': 'BRANCH',
                'Value': 'main'
            }
        },
        'ImageRepository': {
            'ImageConfiguration': {
                'RuntimeEnvironmentVariables': {
                    'KEY': 'VALUE'
                }
            }
        }
    }
)

print(response)

Official reference: AWS App Runner Documentation


94. What is Amazon Managed Workflows for Apache Airflow (MWAA) and how does it simplify the process of orchestrating complex data workflows in a distributed system?

Amazon MWAA is a fully managed service that makes it easy to run Apache Airflow, a popular open-source platform for authoring, scheduling, and monitoring workflows. It simplifies the orchestration of data workflows. Here’s a Python code snippet using boto3 to create an MWAA environment:

import boto3

mwaa = boto3.client('mwaa')

# Create an MWAA environment
response = mwaa.create_environment(
    Name='my-environment',
    DagS3Path='s3://my-bucket/dags',
    ExecutionRoleArn='arn:aws:iam::123456789012:role/service-role/MWAA-ExecutionRole',
    WebserverAccessMode='PRIVATE_ONLY',
    NetworkConfiguration={
        'SubnetIds': [
            'subnet-0123456789abcdef0',
            'subnet-0123456789abcdef1'
        ],
        'SecurityGroupIds': [
            'sg-0123456789abcdef0'
        ]
    }
)

print(response)

Official reference: Amazon Managed Workflows for Apache Airflow Documentation


95. How does AWS X-Ray simplify the process of tracing and analyzing distributed applications in a distributed system?

AWS X-Ray is a service that helps you analyze and debug distributed applications, such as those built using a microservices architecture. It provides end-to-end visibility. Here’s a Python code snippet using boto3 to enable X-Ray tracing for an AWS Lambda function:

import boto3

xray = boto3.client('xray')

# Enable X-Ray tracing for a Lambda function
response = xray.put_tracing_config(
    Name='LambdaFunctionTracing',
    Type='Lambda',
    Value='Active'
)

print(response)

Official reference: AWS X-Ray Documentation


96. Explain the purpose of AWS Glue in a distributed system.

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for you to prepare and load your data for analytics. It’s designed for data lakes and data warehouses. Here’s a Python code snippet using boto3 to create a Glue job:

import boto3

glue = boto3.client('glue')

# Create a Glue job
response = glue.create_job(
    Name='my-glue-job',
    Role='arn:aws:iam::123456789012:role/service-role/GlueServiceRole',
    Command={
        'Name': 'glueetl',
        'ScriptLocation': 's3://my-bucket/glue-script.py'
    },
    DefaultArguments={
        '--job-language': 'python'
    },
    GlueVersion='2.0',
    MaxCapacity=0.0625,
    MaxRetries=0,
    Timeout=10,
    WorkerType='Standard',
    NumberOfWorkers=2
)

print(response)

Official reference: AWS Glue Documentation


97. How does AWS Key Management Service (KMS) enhance security for sensitive data in a distributed system?

AWS Key Management Service (KMS) makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and in your applications. It enhances security for sensitive data. Here’s a Python code snippet using boto3 to create a KMS key:

import boto3

kms = boto3.client('kms')

# Create a KMS key
response = kms.create_key(
    Description='my-key',
    KeyUsage='ENCRYPT_DECRYPT',
    Origin='AWS_KMS',
    Policy='...'
)

print(response)

Official reference: AWS Key Management Service Documentation


98. What is AWS CloudTrail and how does it enhance governance, compliance, and operational auditing in a distributed system?

AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of your AWS account. It records AWS API calls. Here’s a Python code snippet using boto3 to create a CloudTrail trail:

import boto3

cloudtrail = boto3.client('cloudtrail')

# Create a CloudTrail trail
response = cloudtrail.create_trail(
    Name='my-trail',
    S3BucketName='my-bucket',
    IsMultiRegionTrail=True
)

print(response)

Official reference: AWS CloudTrail Documentation


99. How does Amazon SNS (Simple Notification Service) simplify the process of building applications that send messages in a distributed system?

Amazon SNS is a fully managed messaging service for both application-to-application (A2A) and application-to-person (A2P) communication. It simplifies the process of sending messages. Here’s a Python code snippet using boto3 to publish a message to an SNS topic:

import boto3

sns = boto3.client('sns')

# Publish a message to an SNS topic
response = sns.publish(
    TopicArn='arn:aws:sns:us-west-2:123456789012:my-topic',
    Message='Hello, World!'
)

print(response)

Official reference: Amazon SNS Documentation


100. Explain the purpose of Amazon RDS (Relational Database Service) in a distributed system.

Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud. It supports multiple database engines. Here’s a Python code snippet using boto3 to create an RDS instance:

import boto3

rds = boto3.client('rds')

# Create an RDS instance
response = rds.create_db_instance(
    DBInstanceIdentifier='my-db-instance',
    Engine='mysql',
    AllocatedStorage=20,
    DBInstanceClass='db.t2.micro',
    MasterUsername='myuser',
    MasterUserPassword='mypassword',
    AvailabilityZone='us-west-2a',
    BackupRetentionPeriod=7,
    MultiAZ=False,
    StorageType='gp2',
    VpcSecurityGroupIds=[
        'sg-0123456789abcdef0'
    ]
)

print(response)

Official reference: Amazon RDS Documentation