Spark MLlib Basics MCQ Questions with Answers (Latest 2026)

Q1. Which option best describes MLlib?

A. Spark's distributed machine learning library. B. A pure SQL engine. C. A pure cache. D. A pure scheduler.

Select an answer to check.

Answer: Spark's distributed machine learning library.

Here, Spark's distributed machine learning library. is the right choice. DataFrame-based API (spark.ml). It aligns directly with what the question asks about which option best describes mllib. A quick elimination of partially true options helps confirm it.

Q2. What is the primary purpose of MLlib?

A. A pure scheduler. B. A pure SQL engine. C. Spark's distributed machine learning library. D. A pure cache.

Select an answer to check.

Answer: Spark's distributed machine learning library.

In this case, Spark's distributed machine learning library. is correct. DataFrame-based API (spark.ml). It aligns directly with what the question asks about what is the primary purpose of mllib. A quick elimination of partially true options helps confirm it.

Q3. Which statement about MLlib is most accurate?

A. A pure cache. B. A pure SQL engine. C. Spark's distributed machine learning library. D. A pure scheduler.

Select an answer to check.

Answer: Spark's distributed machine learning library.

The best option here is Spark's distributed machine learning library.. DataFrame-based API (spark.ml). It aligns directly with what the question asks about which statement about mllib is most accurate. A quick elimination of partially true options helps confirm it.

Q4. How is MLlib best characterized?

A. A pure cache. B. A pure SQL engine. C. A pure scheduler. D. Spark's distributed machine learning library.

Select an answer to check.

Answer: Spark's distributed machine learning library.

For this question, Spark's distributed machine learning library. is correct. DataFrame-based API (spark.ml). It aligns directly with what the question asks about how is mllib best characterized. A quick elimination of partially true options helps confirm it.

Q5. Which option best describes the DataFrame ML API?

A. RDD-based mllib. B. Pure pandas only. C. Pure scikit-learn. D. spark.ml package built on DataFrames.

Select an answer to check.

Answer: spark.ml package built on DataFrames.

spark.ml package built on DataFrames. is the correct answer here. Modern API. It aligns directly with what the question asks about which option best describes the dataframe ml api. A quick elimination of partially true options helps confirm it.

Q6. What is the primary purpose of the DataFrame ML API?

A. Pure pandas only. B. RDD-based mllib. C. spark.ml package built on DataFrames. D. Pure scikit-learn.

Select an answer to check.

Answer: spark.ml package built on DataFrames.

Here, spark.ml package built on DataFrames. is the right choice. Modern API. This matches the core idea being tested around what is the primary purpose of the dataframe. A quick elimination of partially true options helps confirm it.

Q7. Which statement about the DataFrame ML API is most accurate?

A. spark.ml package built on DataFrames. B. Pure pandas only. C. Pure scikit-learn. D. RDD-based mllib.

Select an answer to check.

Answer: spark.ml package built on DataFrames.

In this case, spark.ml package built on DataFrames. is correct. Modern API. This matches the core idea being tested around which statement about the dataframe ml api is. A quick elimination of partially true options helps confirm it.

Q8. How is the DataFrame ML API best characterized?

A. RDD-based mllib. B. Pure scikit-learn. C. spark.ml package built on DataFrames. D. Pure pandas only.

Select an answer to check.

Answer: spark.ml package built on DataFrames.

The best option here is spark.ml package built on DataFrames.. Modern API. This matches the core idea being tested around how is the dataframe ml api best characterized. A quick elimination of partially true options helps confirm it.

Q9. Which option best describes a Transformer?

A. A pure Estimator. B. A pure Pipeline. C. Algorithm that transforms one DF into another. D. A pure Model.

Select an answer to check.

Answer: Algorithm that transforms one DF into another.

For this question, Algorithm that transforms one DF into another. is correct. Has a transform() method. This matches the core idea being tested around which option best describes a transformer. A quick elimination of partially true options helps confirm it.

Q10. What is the primary purpose of a Transformer?

A. A pure Model. B. Algorithm that transforms one DF into another. C. A pure Estimator. D. A pure Pipeline.

Select an answer to check.

Answer: Algorithm that transforms one DF into another.

Algorithm that transforms one DF into another. is the correct answer here. Has a transform() method. This matches the core idea being tested around what is the primary purpose of a transformer. A quick elimination of partially true options helps confirm it.

Q11. Which statement about a Transformer is most accurate?

A. Algorithm that transforms one DF into another. B. A pure Model. C. A pure Pipeline. D. A pure Estimator.

Select an answer to check.

Answer: Algorithm that transforms one DF into another.

Here, Algorithm that transforms one DF into another. is the right choice. Has a transform() method. That is exactly the concept behind which statement about a transformer is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q12. How is a Transformer best characterized?

A. A pure Estimator. B. A pure Model. C. Algorithm that transforms one DF into another. D. A pure Pipeline.

Select an answer to check.

Answer: Algorithm that transforms one DF into another.

In this case, Algorithm that transforms one DF into another. is correct. Has a transform() method. That is exactly the concept behind how is a transformer best characterized in this context. A quick elimination of partially true options helps confirm it.

Q13. Which option best describes an Estimator?

A. A Transformer. B. Trains on data and produces a Model. C. A pure Pipeline. D. A pure Param.

Select an answer to check.

Answer: Trains on data and produces a Model.

The best option here is Trains on data and produces a Model.. Has a fit() method. That is exactly the concept behind which option best describes an estimator in this context. A quick elimination of partially true options helps confirm it.

Q14. What is the primary purpose of an Estimator?

A. A pure Param. B. A pure Pipeline. C. Trains on data and produces a Model. D. A Transformer.

Select an answer to check.

Answer: Trains on data and produces a Model.

For this question, Trains on data and produces a Model. is correct. Has a fit() method. That is exactly the concept behind what is the primary purpose of an estimator in this context. A quick elimination of partially true options helps confirm it.

Q15. Which statement about an Estimator is most accurate?

A. Trains on data and produces a Model. B. A Transformer. C. A pure Pipeline. D. A pure Param.

Select an answer to check.

Answer: Trains on data and produces a Model.

Trains on data and produces a Model. is the correct answer here. Has a fit() method. That is exactly the concept behind which statement about an estimator is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q16. How is an Estimator best characterized?

A. A pure Param. B. A Transformer. C. Trains on data and produces a Model. D. A pure Pipeline.

Select an answer to check.

Answer: Trains on data and produces a Model.

Here, Trains on data and produces a Model. is the right choice. Has a fit() method. It fits the requirement in the prompt about how is an estimator best characterized. A quick elimination of partially true options helps confirm it.

Q17. Which option best describes a Model?

A. A pure Pipeline. B. A pure Param. C. An Estimator. D. Trained Transformer producing predictions.

Select an answer to check.

Answer: Trained Transformer producing predictions.

In this case, Trained Transformer producing predictions. is correct. Result of fit(). It fits the requirement in the prompt about which option best describes a model. A quick elimination of partially true options helps confirm it.

Q18. What is the primary purpose of a Model?

A. Trained Transformer producing predictions. B. A pure Param. C. An Estimator. D. A pure Pipeline.

Select an answer to check.

Answer: Trained Transformer producing predictions.

The best option here is Trained Transformer producing predictions.. Result of fit(). It fits the requirement in the prompt about what is the primary purpose of a model. A quick elimination of partially true options helps confirm it.

Q19. Which statement about a Model is most accurate?

A. A pure Param. B. An Estimator. C. A pure Pipeline. D. Trained Transformer producing predictions.

Select an answer to check.

Answer: Trained Transformer producing predictions.

For this question, Trained Transformer producing predictions. is correct. Result of fit(). It fits the requirement in the prompt about which statement about a model is most accurate. A quick elimination of partially true options helps confirm it.

Q20. How is a Model best characterized?

A. A pure Pipeline. B. Trained Transformer producing predictions. C. A pure Param. D. An Estimator.

Select an answer to check.

Answer: Trained Transformer producing predictions.

Trained Transformer producing predictions. is the correct answer here. Result of fit(). It fits the requirement in the prompt about how is a model best characterized. A quick elimination of partially true options helps confirm it.

Q21. Which option best describes a Pipeline?

A. A pure Param. B. A pure Model. C. Sequence of stages (Transformers/Estimators). D. A single Estimator.

Select an answer to check.

Answer: Sequence of stages (Transformers/Estimators).

Here, Sequence of stages (Transformers/Estimators). is the right choice. Fit() turns it into PipelineModel. This is the most accurate statement for which option best describes a pipeline. A quick elimination of partially true options helps confirm it.

Q22. What is the primary purpose of a Pipeline?

A. Sequence of stages (Transformers/Estimators). B. A pure Model. C. A pure Param. D. A single Estimator.

Select an answer to check.

Answer: Sequence of stages (Transformers/Estimators).

In this case, Sequence of stages (Transformers/Estimators). is correct. Fit() turns it into PipelineModel. This is the most accurate statement for what is the primary purpose of a pipeline. A quick elimination of partially true options helps confirm it.

Q23. Which statement about a Pipeline is most accurate?

A. Sequence of stages (Transformers/Estimators). B. A pure Model. C. A pure Param. D. A single Estimator.

Select an answer to check.

Answer: Sequence of stages (Transformers/Estimators).

The best option here is Sequence of stages (Transformers/Estimators).. Fit() turns it into PipelineModel. This is the most accurate statement for which statement about a pipeline is most accurate. A quick elimination of partially true options helps confirm it.

Q24. How is a Pipeline best characterized?

A. A pure Model. B. A pure Param. C. Sequence of stages (Transformers/Estimators). D. A single Estimator.

Select an answer to check.

Answer: Sequence of stages (Transformers/Estimators).

For this question, Sequence of stages (Transformers/Estimators). is correct. Fit() turns it into PipelineModel. This is the most accurate statement for how is a pipeline best characterized. A quick elimination of partially true options helps confirm it.

Q25. Which option best describes VectorAssembler?

A. A pure label encoder. B. A pure scaler. C. A pure tokenizer. D. Combine columns into a feature vector.

Select an answer to check.

Answer: Combine columns into a feature vector.

Combine columns into a feature vector. is the correct answer here. Common feature step. This is the most accurate statement for which option best describes vectorassembler. A quick elimination of partially true options helps confirm it.

Q26. What is the primary purpose of VectorAssembler?

A. A pure tokenizer. B. Combine columns into a feature vector. C. A pure label encoder. D. A pure scaler.

Select an answer to check.

Answer: Combine columns into a feature vector.

Here, Combine columns into a feature vector. is the right choice. Common feature step. It aligns directly with what the question asks about what is the primary purpose of vectorassembler. The other options are either incomplete or contextually incorrect.

Q27. Which statement about VectorAssembler is most accurate?

A. A pure tokenizer. B. A pure label encoder. C. A pure scaler. D. Combine columns into a feature vector.

Select an answer to check.

Answer: Combine columns into a feature vector.

In this case, Combine columns into a feature vector. is correct. Common feature step. It aligns directly with what the question asks about which statement about vectorassembler is most accurate. The other options are either incomplete or contextually incorrect.

Q28. How is VectorAssembler best characterized?

A. A pure tokenizer. B. A pure scaler. C. Combine columns into a feature vector. D. A pure label encoder.

Select an answer to check.

Answer: Combine columns into a feature vector.

The best option here is Combine columns into a feature vector.. Common feature step. It aligns directly with what the question asks about how is vectorassembler best characterized. The other options are either incomplete or contextually incorrect.

Q29. Which option best describes StandardScaler?

A. Standardize features (mean 0, unit variance). B. Pure imputer. C. MinMax scaler. D. Robust scaler.

Select an answer to check.

Answer: Standardize features (mean 0, unit variance).

For this question, Standardize features (mean 0, unit variance). is correct. For algorithms sensitive to scale. It aligns directly with what the question asks about which option best describes standardscaler. The other options are either incomplete or contextually incorrect.

Q30. What is the primary purpose of StandardScaler?

A. Pure imputer. B. MinMax scaler. C. Standardize features (mean 0, unit variance). D. Robust scaler.

Select an answer to check.

Answer: Standardize features (mean 0, unit variance).

Standardize features (mean 0, unit variance). is the correct answer here. For algorithms sensitive to scale. It aligns directly with what the question asks about what is the primary purpose of standardscaler. The other options are either incomplete or contextually incorrect.

Q31. Which statement about StandardScaler is most accurate?

A. Standardize features (mean 0, unit variance). B. Robust scaler. C. MinMax scaler. D. Pure imputer.

Select an answer to check.

Answer: Standardize features (mean 0, unit variance).

Here, Standardize features (mean 0, unit variance). is the right choice. For algorithms sensitive to scale. This matches the core idea being tested around which statement about standardscaler is most accurate. The other options are either incomplete or contextually incorrect.

Q32. How is StandardScaler best characterized?

A. MinMax scaler. B. Pure imputer. C. Robust scaler. D. Standardize features (mean 0, unit variance).

Select an answer to check.

Answer: Standardize features (mean 0, unit variance).

In this case, Standardize features (mean 0, unit variance). is correct. For algorithms sensitive to scale. This matches the core idea being tested around how is standardscaler best characterized. The other options are either incomplete or contextually incorrect.

Q33. Which option best describes StringIndexer?

A. OneHotEncoder. B. Pure tokenizer. C. Pure scaler. D. Encode strings to numeric indices.

Select an answer to check.

Answer: Encode strings to numeric indices.

The best option here is Encode strings to numeric indices.. Often paired with OneHot. This matches the core idea being tested around which option best describes stringindexer. The other options are either incomplete or contextually incorrect.

Q34. What is the primary purpose of StringIndexer?

A. OneHotEncoder. B. Pure tokenizer. C. Encode strings to numeric indices. D. Pure scaler.

Select an answer to check.

Answer: Encode strings to numeric indices.

For this question, Encode strings to numeric indices. is correct. Often paired with OneHot. This matches the core idea being tested around what is the primary purpose of stringindexer. The other options are either incomplete or contextually incorrect.

Q35. Which statement about StringIndexer is most accurate?

A. Encode strings to numeric indices. B. OneHotEncoder. C. Pure tokenizer. D. Pure scaler.

Select an answer to check.

Answer: Encode strings to numeric indices.

Encode strings to numeric indices. is the correct answer here. Often paired with OneHot. This matches the core idea being tested around which statement about stringindexer is most accurate. The other options are either incomplete or contextually incorrect.

Q36. How is StringIndexer best characterized?

A. OneHotEncoder. B. Encode strings to numeric indices. C. Pure scaler. D. Pure tokenizer.

Select an answer to check.

Answer: Encode strings to numeric indices.

Here, Encode strings to numeric indices. is the right choice. Often paired with OneHot. That is exactly the concept behind how is stringindexer best characterized in this context. The other options are either incomplete or contextually incorrect.

Q37. Which option best describes OneHotEncoder?

A. Convert indices to one-hot vectors. B. Pure scaler. C. StringIndexer. D. Pure tokenizer.

Select an answer to check.

Answer: Convert indices to one-hot vectors.

In this case, Convert indices to one-hot vectors. is correct. For categorical features. That is exactly the concept behind which option best describes onehotencoder in this context. The other options are either incomplete or contextually incorrect.

Q38. What is the primary purpose of OneHotEncoder?

A. Convert indices to one-hot vectors. B. Pure tokenizer. C. Pure scaler. D. StringIndexer.

Select an answer to check.

Answer: Convert indices to one-hot vectors.

The best option here is Convert indices to one-hot vectors.. For categorical features. That is exactly the concept behind what is the primary purpose of onehotencoder in this context. The other options are either incomplete or contextually incorrect.

Q39. Which statement about OneHotEncoder is most accurate?

A. Pure tokenizer. B. StringIndexer. C. Convert indices to one-hot vectors. D. Pure scaler.

Select an answer to check.

Answer: Convert indices to one-hot vectors.

For this question, Convert indices to one-hot vectors. is correct. For categorical features. That is exactly the concept behind which statement about onehotencoder is most accurate in this context. The other options are either incomplete or contextually incorrect.

Q40. How is OneHotEncoder best characterized?

A. Pure scaler. B. StringIndexer. C. Convert indices to one-hot vectors. D. Pure tokenizer.

Select an answer to check.

Answer: Convert indices to one-hot vectors.

Convert indices to one-hot vectors. is the correct answer here. For categorical features. That is exactly the concept behind how is onehotencoder best characterized in this context. The other options are either incomplete or contextually incorrect.

Q41. Which option best describes Tokenizer?

A. Pure index. B. Split text into tokens. C. Pure scaler. D. StopWordsRemover.

Select an answer to check.

Answer: Split text into tokens.

Here, Split text into tokens. is the right choice. NLP preprocessing. It fits the requirement in the prompt about which option best describes tokenizer. The other options are either incomplete or contextually incorrect.

Q42. What is the primary purpose of Tokenizer?

A. StopWordsRemover. B. Pure scaler. C. Pure index. D. Split text into tokens.

Select an answer to check.

Answer: Split text into tokens.

In this case, Split text into tokens. is correct. NLP preprocessing. It fits the requirement in the prompt about what is the primary purpose of tokenizer. The other options are either incomplete or contextually incorrect.

Q43. Which statement about Tokenizer is most accurate?

A. StopWordsRemover. B. Pure scaler. C. Pure index. D. Split text into tokens.

Select an answer to check.

Answer: Split text into tokens.

The best option here is Split text into tokens.. NLP preprocessing. It fits the requirement in the prompt about which statement about tokenizer is most accurate. The other options are either incomplete or contextually incorrect.

Q44. How is Tokenizer best characterized?

A. Pure index. B. Pure scaler. C. Split text into tokens. D. StopWordsRemover.

Select an answer to check.

Answer: Split text into tokens.

For this question, Split text into tokens. is correct. NLP preprocessing. It fits the requirement in the prompt about how is tokenizer best characterized. The other options are either incomplete or contextually incorrect.

Q45. Which option best describes HashingTF / IDF?

A. Pure index. B. Compute TF-IDF features. C. Pure scaler. D. Pure tokenizer.

Select an answer to check.

Answer: Compute TF-IDF features.

Compute TF-IDF features. is the correct answer here. Text features. It fits the requirement in the prompt about which option best describes hashingtf / idf. The other options are either incomplete or contextually incorrect.

Q46. What is the primary purpose of HashingTF / IDF?

A. Pure index. B. Pure scaler. C. Compute TF-IDF features. D. Pure tokenizer.

Select an answer to check.

Answer: Compute TF-IDF features.

Here, Compute TF-IDF features. is the right choice. Text features. This is the most accurate statement for what is the primary purpose of hashingtf /. The other options are either incomplete or contextually incorrect.

Q47. Which statement about HashingTF / IDF is most accurate?

A. Pure tokenizer. B. Pure scaler. C. Pure index. D. Compute TF-IDF features.

Select an answer to check.

Answer: Compute TF-IDF features.

In this case, Compute TF-IDF features. is correct. Text features. This is the most accurate statement for which statement about hashingtf / idf is most. The other options are either incomplete or contextually incorrect.

Q48. How is HashingTF / IDF best characterized?

A. Pure tokenizer. B. Pure index. C. Pure scaler. D. Compute TF-IDF features.

Select an answer to check.

Answer: Compute TF-IDF features.

The best option here is Compute TF-IDF features.. Text features. This is the most accurate statement for how is hashingtf / idf best characterized. The other options are either incomplete or contextually incorrect.

Q49. Which option best describes LogisticRegression?

A. Linear classifier. B. Random forest. C. K-means. D. Decision tree.

Select an answer to check.

Answer: Linear classifier.

For this question, Linear classifier. is correct. Binary or multinomial. This is the most accurate statement for which option best describes logisticregression. The other options are either incomplete or contextually incorrect.

Q50. What is the primary purpose of LogisticRegression?

A. K-means. B. Linear classifier. C. Decision tree. D. Random forest.

Select an answer to check.

Answer: Linear classifier.

Linear classifier. is the correct answer here. Binary or multinomial. This is the most accurate statement for what is the primary purpose of logisticregression. The other options are either incomplete or contextually incorrect.