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Top 100 Big Data Interview Questions and Answers

Top 100 Big Data Interview Questions and Answers
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1. What is Big Data?

Answer:
Big Data refers to a vast volume of structured, semi-structured, and unstructured data that is too large to be processed by traditional database management systems. It encompasses data with high volume, velocity, and variety.

Official Reference: What is Big Data?


2. Explain the three Vs of Big Data.

Answer:
The three Vs of Big Data are:

  • Volume: Refers to the immense amount of data generated daily.
  • Velocity: Describes the speed at which new data is generated and needs to be processed.
  • Variety: Encompasses the diverse types of data, including structured, semi-structured, and unstructured.

Official Reference: The 3Vs of Big Data


3. What is Hadoop?

Answer:
Hadoop is an open-source framework designed to store and process large volumes of data across a distributed cluster of commodity hardware. It provides a reliable and scalable solution for handling Big Data.

Official Reference: Apache Hadoop


4. What is MapReduce?

Answer:
MapReduce is a programming model and processing engine designed for processing and generating large datasets in parallel across a distributed cluster.

Official Reference: MapReduce


5. Provide an example of a MapReduce job.

Answer:

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
  public void map(LongWritable key, Text value, Context context)
      throws IOException, InterruptedException {
    String line = value.toString();
    String[] words = line.split(" ");
    for (String word : words) {
      context.write(new Text(word), new IntWritable(1));
    }
  }
}

This is a simple Word Count Mapper in Hadoop. It reads input, splits it into words, and emits key-value pairs.

Official Reference: Word Count Example


6. Explain the difference between HDFS and YARN.

Answer:

  • HDFS (Hadoop Distributed File System): HDFS is the storage layer of Hadoop, designed to store and manage data across a distributed cluster.
  • YARN (Yet Another Resource Negotiator): YARN is the resource management layer responsible for managing and allocating resources in a Hadoop cluster.

Official Reference: HDFS vs. YARN


7. What is Apache Spark?

Answer:
Apache Spark is an open-source distributed computing system that provides a fast and general-purpose cluster-computing framework for Big Data processing. It supports a wide range of applications including batch processing, real-time streaming, machine learning, and graph processing.

Official Reference: Apache Spark


8. Explain the concept of RDD in Apache Spark.

Answer:
RDD (Resilient Distributed Dataset) is the fundamental data structure of Apache Spark. It represents a distributed collection of objects that can be processed in parallel. RDDs are immutable and fault-tolerant, allowing for distributed processing across a cluster.

Official Reference: RDD in Spark


9. Provide an example of creating an RDD in Spark.

Answer:

val data = Array(1, 2, 3, 4, 5)
val rdd = sc.parallelize(data)

In this example, data is an array, and sc.parallelize() is used to create an RDD from the array.

Official Reference: Creating RDDs


10. What is the difference between transformation and action in Apache Spark?

Answer:

  • Transformation: Transformations are operations on RDDs that return a new RDD. They are lazily evaluated, meaning they don’t compute their results right away, but rather remember the transformation to be applied.
  • Action: Actions are operations on RDDs that trigger the execution of the Spark computation. They return a result to the driver program or write it to storage.

Official Reference: Transformations and Actions


11. Explain what is meant by the term “Lazy Evaluation” in Spark.

Answer:
Lazy Evaluation in Spark means that the execution of transformations is deferred until an action is triggered. This allows Spark to optimize the execution plan and perform optimizations like pipelining transformations together.

Official Reference: Lazy Evaluation


12. What is a Spark DataFrame?

Answer:
A Spark DataFrame is a distributed collection of data organized into columns. It is similar to a table in a relational database, or a data frame in R/Python. It allows for efficient querying and processing of structured data.

Official Reference: Spark DataFrames


13. What is the significance of a Spark SQL Context?

Answer:
A Spark SQL Context is the entry point for using Spark SQL, which allows for integrating SQL queries with Spark programs. It provides a unified interface to access different data sources and perform SQL-like operations on distributed data.

Official Reference: Spark SQL Context


14. Provide an example of creating a DataFrame in Spark.

Answer:

val data = Seq(("John", 25), ("Jane", 30), ("Jim", 28))
val df = spark.createDataFrame(data).toDF("Name", "Age")

In this example, a sequence of tuples data is converted into a DataFrame df with columns “Name” and “Age”.

Official Reference: Creating DataFrames


15. What is a Spark Executor?

Answer:
A Spark Executor is a process responsible for executing tasks on a node. It reads data from storage, processes it, and stores the results back in memory or disk. Each Spark application has its own set of executors.

Official Reference: Spark Executors


16. Explain the concept of a Spark Task.

Answer:
A Spark Task is a unit of work that is sent to an Executor for processing. Tasks are created for each partition of an RDD and are responsible for processing that partition’s data.

Official Reference: Spark Tasks


17. What is the role of a Spark Driver in a Spark application?

Answer:
The Spark Driver is the program that runs the main() function and creates the SparkContext, thus orchestrating the execution of a Spark application. It converts user code into tasks and schedules them for execution.

Official Reference: Spark Driver


18. Explain the concept of a Spark DAG (Directed Acyclic Graph).

Answer:
A Spark DAG represents the flow of data and transformations in a Spark application. It’s a logical plan that represents the sequence of operations to be executed on the data.

Official Reference: Spark DAG


19. What is the purpose of a Spark Stage?

Answer:
A Spark Stage is a physical unit of execution that represents a set of tasks that can be executed together as a result of a set of transformations. Stages are determined by the boundaries of shuffles or the input data.

Official Reference: Spark Stages


20. Explain what a Spark Shuffle is.

Answer:
A Spark Shuffle is the process of redistributing data across the cluster during certain operations like aggregations or joins. It involves exchanging data between executors and is a costly operation in terms of performance.

Official Reference: Spark Shuffle


21. What is the purpose of a Spark Partition?

Answer:
A Spark Partition is a logical division of data within an RDD or DataFrame. It determines the unit of parallelism in Spark, with each partition processed by a separate task.

Official Reference: Spark Partitions


22. Explain the concept of Data Locality in Spark.

Answer:
Data Locality in Spark refers to the principle of scheduling tasks on the same nodes where the data they work on is stored. This minimizes data transfer over the network, improving performance.

Official Reference: Data Locality


23. What is a Broadcast Variable in Spark?

Answer:
A Broadcast Variable in Spark is a read-only variable that is cached on each machine in the cluster rather than being shipped with tasks. It’s useful for efficiently sharing large, read-only data sets across tasks.

Official Reference: Broadcast Variables


24. Explain what Accumulators are in Spark.

Answer:
Accumulators in Spark are variables that can only be added to through an associative operation and can be efficiently supported in parallel. They are used for aggregating information across the cluster.

Official Reference: Accumulators


25. What is the purpose of a Spark Broadcast Join?

Answer:
A Spark Broadcast Join is a type of join operation where one of the DataFrames or RDDs is small enough to fit in the memory of each worker node. This allows for a more efficient join operation as the smaller DataFrame is broadcasted to all nodes.

Official Reference: Broadcast Join


26. Explain the significance of the groupBy and agg operations in Spark.

Answer:
The groupBy operation in Spark is used to group the DataFrame or RDD data based on one or more columns. The agg operation (short for aggregate) is then used to perform aggregation functions like count, sum, avg, etc., on the grouped data.

Official Reference:


27. What is the purpose of a Spark UDF?

Answer:
A Spark UDF (User-Defined Function) is a custom function that is defined by the user to perform specific operations on DataFrame or RDD columns. UDFs allow for more flexibility in data manipulation.

Official Reference: UDFs


28. Explain the concept of Spark Window Functions.

Answer:
Spark Window Functions allow users to perform calculations across a set of table rows related to the current row. They are typically used in scenarios where you need to perform calculations over a sliding window of data.

Official Reference: Window Functions


29. What is the purpose of a Spark Transformer in MLlib?

Answer:
In MLlib (Spark’s machine learning library), a Transformer is an algorithm or computation that transforms one DataFrame into another. It represents data transformations like feature extraction, transformation, and more.

Official Reference: Transformer


30. Explain the significance of a Spark Estimator in MLlib.

Answer:
In MLlib, an Estimator is an algorithm or computation that fits or trains on a DataFrame to produce a Model. It represents learning algorithms such as regression, classification, clustering, etc.

Official Reference: Estimator


31. What is the purpose of a Spark ML Pipeline?

Answer:
A Spark ML Pipeline is an API that allows users to combine multiple algorithms into a single workflow. It includes feature extraction, transformations, and the final estimator. This streamlines the process and ensures consistent data processing.

Official Reference: ML Pipelines


32. Explain the concept of Hyperparameter Tuning in Spark MLlib.

Answer:
Hyperparameter Tuning in MLlib refers to the process of finding the best set of hyperparameters for a machine learning model. This is often done using techniques like Grid Search or Random Search.

Official Reference: Hyperparameter Tuning


33. What is the purpose of a Spark ML Model?

Answer:
A Spark ML Model is the result of applying an Estimator to a DataFrame. It encapsulates the learned information from the data and can be used for making predictions on new data.

Official Reference: ML Models


34. Explain the significance of a Spark ML Transformer in a Pipeline.

Answer:
In a Spark ML Pipeline, a Transformer takes a DataFrame and produces a new DataFrame with a specified transformation. It plays a crucial role in feature extraction and manipulation before applying the final Estimator.

Official Reference: ML Transformers


35. What is the purpose of a Spark ML Estimator in a Pipeline?

Answer:
In a Spark ML Pipeline, an Estimator is a learning algorithm or any algorithm that fits or trains on data to produce a Model. It’s the final step in the pipeline and responsible for generating the model.

Official Reference: ML Estimators


36. Explain the concept of Feature Engineering in Machine Learning using Spark.

Answer:
Feature Engineering in Spark ML involves creating new features or modifying existing ones to improve the performance of machine learning models. It includes tasks like one-hot encoding, scaling, and creating interaction terms.

Official Reference: Feature Engineering


37. What is the purpose of a Spark ML VectorAssembler?

Answer:
A VectorAssembler in Spark ML is a Transformer that combines a given list of columns into a single feature vector. This is useful when working with machine learning algorithms that expect a single input column.

Official Reference: VectorAssembler


38. Explain the concept of a Spark ML StringIndexer.

Answer:
A StringIndexer in Spark ML is a Transformer that encodes a string column of labels to a column of label indices. This is particularly useful when working with categorical features that need to be converted into numerical values for machine learning algorithms.

Official Reference: StringIndexer


39. What is the purpose of a Spark ML OneHotEncoder?

Answer:
A OneHotEncoder in Spark ML is a Transformer that converts categorical variables into a binary vector format. It takes a column with category indices and maps it to a binary vector, where only one element is ‘1’ (hot) while the rest are ‘0’ (cold).

Official Reference: OneHotEncoder


40. Explain the significance of a Spark ML StandardScaler.

Answer:
A StandardScaler in Spark ML is a Transformer that standardizes features by removing the mean and scaling to unit variance. This ensures that the features have zero mean and unit variance, which is important for algorithms sensitive to the scale of features.

Official Reference: StandardScaler


41. What is the purpose of a Spark ML MinMaxScaler?

Answer:
A MinMaxScaler in Spark ML is a Transformer that transforms a dataset of Vector rows, rescaling each feature to a specific range (by default [0, 1]). This is particularly useful for algorithms that are sensitive to feature scales.

Official Reference: MinMaxScaler


42. Explain the concept of a Spark ML PCA (Principal Component Analysis).

Answer:
PCA in Spark ML is a statistical procedure that uses an orthogonal transformation to convert a set of observations into a new coordinate system. It is used for dimensionality reduction and feature extraction.

Official Reference: PCA


43. What is the purpose of a Spark ML Bucketizer?

Answer:
A Bucketizer in Spark ML is a Transformer that maps a column of continuous features to a column of feature buckets, where the buckets are specified by users. It’s commonly used for discretization of continuous features.

Official Reference: Bucketizer


44. Explain the concept of a Spark ML Imputer.

Answer:
An Imputer in Spark ML is a Transformer that completes missing values in a dataset either using the mean or the median of the columns with missing values. It’s a crucial step in data preprocessing.

Official Reference: Imputer


45. What is the purpose of a Spark ML SQLTransformer?

Answer:
A SQLTransformer in Spark ML is a Transformer that allows users to apply SQL-like transformations to a DataFrame. It enables easy feature engineering using SQL expressions.

Official Reference: SQLTransformer


46. Explain the purpose of a Spark ML VectorAssembler.

Answer:
A VectorAssembler in Spark ML is a Transformer that combines a given list of columns into a single vector column. This is particularly useful when working with machine learning algorithms that expect all features to be in a single vector.

Official Reference: VectorAssembler


47. What is the significance of a Spark ML VectorIndexer?

Answer:
A VectorIndexer in Spark ML is a Transformer that automatically identifies categorical features and indexes them. This is important for algorithms that require categorical features to be converted to numerical form.

Official Reference: VectorIndexer


48. Explain the concept of a Spark ML Interaction Transformer.

Answer:
The Interaction Transformer in Spark ML is a feature transformer that takes in a Vector column and outputs a new Vector column with interactions between the input columns. It’s particularly useful for creating interaction terms in regression models.

Official Reference: Interaction


49. What is the purpose of a Spark ML ChiSqSelector?

Answer:
A ChiSqSelector in Spark ML is a feature selector that selects categorical and numerical features based on a Chi-Squared test of independence. It’s useful for feature selection in classification tasks.

Official Reference: ChiSqSelector


50. Explain the significance of a Spark ML ElementwiseProduct.

Answer:
An ElementwiseProduct in Spark ML is a transformer that multiplies each input vector by a provided “weight” vector, effectively scaling the values. It’s used for custom feature scaling.

Official Reference: ElementwiseProduct


51. What is the purpose of a Spark ML PolynomialExpansion?

Answer:
A PolynomialExpansion in Spark ML is a transformer that generates polynomial combinations of the input features. This is useful for non-linear regression models.

Official Reference: PolynomialExpansion


52. Explain the concept of a Spark ML QuantileDiscretizer.

Answer:
A QuantileDiscretizer in Spark ML is a transformer that converts continuous features into categorical features by binning them into a specified number of quantiles. This is useful for decision tree algorithms.

Official Reference: QuantileDiscretizer


53. What is the purpose of a Spark ML RFormula?

Answer:
An RFormula in Spark ML is a feature transformer that creates a vector of features and a double or binary target variable from a formula string. It’s particularly useful for specifying machine learning models in a formulaic style.

Official Reference: RFormula


54. Explain the significance of a Spark ML HashingTF.

Answer:
A HashingTF in Spark ML is a transformer that maps a sequence of terms to their term frequencies using the hashing trick. It’s commonly used for natural language processing tasks.

Official Reference: HashingTF


55. What is the purpose of a Spark ML IDF (Inverse Document Frequency)?

Answer:
An IDF in Spark ML is an estimator that computes the Inverse Document Frequency (IDF) of a collection of documents. It’s used for term weighting in natural language processing tasks.

Official Reference: IDF


56. Explain the concept of a Spark ML Word2Vec.

Answer:
Word2Vec in Spark ML is an algorithm for learning distributed representations of words in a continuous vector space. It’s widely used for natural language processing tasks like sentiment analysis, language translation, and more.

Official Reference: Word2Vec


57. What is the purpose of a Spark ML StopWordsRemover?

Answer:
A StopWordsRemover in Spark ML is a transformer that removes common words (stop words) from a given input text. This is essential for many NLP tasks to focus on more meaningful words.

Official Reference: StopWordsRemover


58. Explain the significance of a Spark ML NGram.

Answer:
An NGram in Spark ML is a transformer that takes a sequence of strings (words) and produces a sequence of n-grams. N-grams are contiguous sequences of n items from a given sample of text or speech.

Official Reference: NGram


59. What is the purpose of a Spark ML CountVectorizer?

Answer:
A CountVectorizer in Spark ML is an estimator that converts a collection of text documents to a matrix of token counts. It’s particularly useful for text classification tasks.

Official Reference: CountVectorizer


60. Explain the concept of a Spark ML FeatureHasher.

Answer:
A FeatureHasher in Spark ML is a transformer that maps a sequence of terms to their term frequencies using the hashing trick. It’s useful for large-scale feature hashing.

Official Reference: FeatureHasher


61. What is the purpose of a Spark ML SQLTransformer?

Answer:
A SQLTransformer in Spark ML allows users to transform a DataFrame using SQL-like statements. It’s useful for performing more complex transformations on DataFrames.

Official Reference: SQLTransformer


62. Explain the significance of a Spark ML VectorSlicer.

Answer:
A VectorSlicer in Spark ML is a transformer that takes a Vector and a list of indices and outputs a new Vector with the selected features. It’s useful for feature selection.

Official Reference: VectorSlicer


63. What is the purpose of a Spark ML Imputer?

Answer:
An Imputer in Spark ML is a transformer that completes missing values in a dataset, either using the mean or median of the columns.

Official Reference: Imputer


64. Explain the concept of a Spark ML MaxAbsScaler.

Answer:
A MaxAbsScaler in Spark ML is a transformer that scales each feature by dividing through the maximum absolute value in each feature. It’s useful for data that is already centered around zero.

Official Reference: MaxAbsScaler


65. What is the purpose of a Spark ML MinMaxScaler?

Answer:
A MinMaxScaler in Spark ML is a transformer that scales each feature to a specific range, typically [0, 1]. It’s useful for algorithms that require features to be on a similar scale.

Official Reference: MinMaxScaler


66. Explain the significance of a Spark ML StandardScaler.

Answer:
A StandardScaler in Spark ML is a transformer that standardizes features by removing the mean and scaling to unit variance. It’s important for algorithms that are sensitive to the scale of features.

Official Reference: StandardScaler


67. What is the purpose of a Spark ML ElementwiseProduct?

Answer:
An ElementwiseProduct in Spark ML is a transformer that multiplies each input vector by a provided “weight” vector, effectively scaling the values. It’s used for custom feature scaling.

Official Reference: ElementwiseProduct


68. Explain the concept of a Spark ML Normalizer.

Answer:
A Normalizer in Spark ML is a transformer that rescales a Vector to have unit norm (a vector of length 1). It’s used in scenarios where the magnitude of a feature vector is less relevant than its direction.

Official Reference: Normalizer


69. What is the purpose of a Spark ML Bucketizer?

Answer:
A Bucketizer in Spark ML is a transformer that maps a continuous feature to a set of “buckets” or ranges. It’s useful for converting continuous data into categorical data.

Official Reference: Bucketizer


70. Explain the significance of a Spark ML QuantileDiscretizer.

Answer:
A QuantileDiscretizer in Spark ML is a transformer that maps a continuous feature to a small number of bins based on the sample quantiles. It’s useful for handling continuous data in algorithms that require categorical features.

Official Reference: QuantileDiscretizer


71. What is the purpose of a Spark ML StringIndexer?

Answer:
A StringIndexer in Spark ML is an estimator that encodes a string column of labels to a column of label indices. It’s often used to convert categorical data to numerical format for machine learning algorithms.

Official Reference: StringIndexer


72. Explain the concept of a Spark ML IndexToString.

Answer:
An IndexToString in Spark ML is a transformer that maps a column of label indices back to a column of original labels. It’s often used after a model has made predictions.

Official Reference: IndexToString


73. What is the purpose of a Spark ML OneHotEncoder?

Answer:
A OneHotEncoder in Spark ML is a transformer that converts categorical variables into a binary vector representation. It’s used when categorical variables need to be included in machine learning models.

Official Reference: OneHotEncoder


74. Explain the significance of a Spark ML VectorAssembler.

Answer:
A VectorAssembler in Spark ML is a transformer that combines a given list of columns into a single vector column. It’s often used to prepare features for machine learning algorithms.

Official Reference: VectorAssembler


75. What is the purpose of a Spark ML VectorSizeHint?

Answer:
A VectorSizeHint in Spark ML is a transformer that sets the size of the vector column in a DataFrame’s schema. It’s useful when the size of the vectors is known in advance.

Official Reference: VectorSizeHint


76. Explain the concept of a Spark ML SQLTransformer.

Answer:
A SQLTransformer in Spark ML allows users to transform a DataFrame using SQL-like statements. It’s useful for performing more complex transformations on DataFrames.

Official Reference: SQLTransformer


77. What is the purpose of a Spark ML BucketedRandomProjectionLSH?

Answer:
A BucketedRandomProjectionLSH in Spark ML is an approximate nearest neighbor search algorithm that hashes input vectors into buckets for efficient search.

Official Reference: BucketedRandomProjectionLSH


78. Explain the concept of a Spark ML MinHashLSH.

Answer:
A MinHashLSH in Spark ML is an approximate nearest neighbor search algorithm that hashes input vectors into buckets for efficient search.

Official Reference: MinHashLSH


79. What is the purpose of a Spark ML LocalitySensitiveHashing?

Answer:
A LocalitySensitiveHashing in Spark ML is an approximate nearest neighbor search algorithm that hashes input vectors into buckets for efficient search.

Official Reference: LocalitySensitiveHashing


80. Explain the significance of a Spark ML ElementwiseProduct.

Answer:
An ElementwiseProduct in Spark ML is a transformer that multiplies each input vector by a provided “weight” vector, effectively scaling the values. It’s used for custom feature scaling.

Official Reference: ElementwiseProduct


81. What is the purpose of a Spark ML SQLTransformer?

Answer:
A SQLTransformer in Spark ML allows users to transform a DataFrame using SQL-like statements. It’s useful for performing more complex transformations on DataFrames.

Official Reference: SQLTransformer


82. What is the significance of a Spark ML Imputer?

Answer:
An Imputer in Spark ML is a transformer that completes missing values in a dataset, either for continuous or categorical features. It replaces missing values with either the mean, median, mode, or a user-specified value.

Official Reference: Imputer


83. Explain the concept of a Spark ML ChiSqSelector.

Answer:
A ChiSqSelector in Spark ML is a feature selector that selects categorical features to use for model training based on a statistical test called the Chi-Squared test.

Official Reference: ChiSqSelector


84. What is the purpose of a Spark ML VarianceThresholdSelector?

Answer:
A VarianceThresholdSelector in Spark ML is a feature selector that selects features based on their variance, with the goal of removing low-variance features.

Official Reference: VarianceThresholdSelector


85. Explain the concept of a Spark ML RFormula.

Answer:
An RFormula in Spark ML is a feature transformer that is used for specifying the formula to use for model training in a symbolic form.

Official Reference: RFormula


86. What is the purpose of a Spark ML Polynomia

Answer:
A PolynomialExpansion in Spark ML is a feature transformer that generates polynomial and interaction features.

Official Reference: PolynomialExpansion


87. Explain the concept of a Spark ML DCT.

Answer:
A DCT (Discrete Cosine Transform) in Spark ML is a feature transformer that performs a DCT on a vector of real values.

Official Reference: DCT


88. What is the purpose of a Spark ML Normalizer?

Answer:
A Normalizer in Spark ML is a transformer that rescales a Vector to have unit norm (a vector of length 1). It’s used in scenarios where the magnitude of a feature vector is less relevant than its direction.

Official Reference: Normalizer


89. Explain the significance of a Spark ML Bucketizer.

Answer:
A Bucketizer in Spark ML is a transformer that maps a continuous feature to a set of “buckets” or ranges. It’s useful for converting continuous data into categorical data.

Official Reference: Bucketizer


90. What is the purpose of a Spark ML QuantileDiscretizer?

Answer:
A QuantileDiscretizer in Spark ML is a transformer that maps a continuous feature to a small number of bins based on the sample quantiles. It’s useful for handling continuous data in algorithms that require categorical features.

Official Reference: QuantileDiscretizer


91. Explain the concept of a Spark ML StringIndexer.

Answer:
A StringIndexer in Spark ML is an estimator that encodes a string column of labels to a column of label indices. It’s often used to convert categorical data to numerical format for machine learning algorithms.

Official Reference: StringIndexer


92. What is the purpose of a Spark ML IndexToString?

Answer:
An IndexToString in Spark ML is a transformer that maps a column of label indices back to a column of original labels. It’s often used after a model has made predictions.

Official Reference: IndexToString


93. Explain the concept of a Spark ML OneHotEncoder.

Answer:
A OneHotEncoder in Spark ML is a transformer that converts categorical variables into a binary vector representation. It’s used when categorical variables need to be included in machine learning models.

Official Reference: OneHotEncoder


94. What is the purpose of a Spark ML VectorAssembler?

Answer:
A VectorAssembler in Spark ML is a transformer that combines a given list of columns into a single vector column. It’s often used to prepare features for machine learning algorithms.

Official Reference: VectorAssembler


95. Explain the concept of a Spark ML SQLTransformer.

Answer:
A SQLTransformer in Spark ML allows users to transform a DataFrame using SQL-like statements. It’s useful for performing more complex transformations on DataFrames.

Official Reference: SQLTransformer


96. What is the significance of a Spark ML Imputer?

Answer:
An Imputer in Spark ML is a transformer that completes missing values in a dataset, either for continuous or categorical features. It replaces missing values with either the mean, median, mode, or a user-specified value.

Official Reference: Imputer


97. Explain the concept of a Spark ML ChiSqSelector.

Answer:
A ChiSqSelector in Spark ML is a feature selector that selects categorical features to use for model training based on a statistical test called the Chi-Squared test.

Official Reference: ChiSqSelector


98. What is the purpose of a Spark ML VarianceThresholdSelector?

Answer:
A VarianceThresholdSelector in Spark ML is a feature selector that selects features based on their variance, with the goal of removing low-variance features.

Official Reference: VarianceThresholdSelector


99. Explain the concept of a Spark ML RFormula.

Answer:
An RFormula in Spark ML is a feature transformer that is used for specifying the formula to use for model training in a symbolic form.

Official Reference: RFormula


100. What is the purpose of a Spark ML PolynomialExpansion?

Answer:
A PolynomialExpansion in Spark ML is a feature transformer that generates polynomial and interaction features.

Official Reference: PolynomialExpansion