Practice Spark MLlib Basics MCQ questions with detailed explanations and clear answer validation. These MCQs help you revise core concepts, compare close options, and improve accuracy for interviews, certification exams, and technical screening rounds. Use this updated 2026 set to strengthen fundamentals and confidence.
Q51. Which statement about LogisticRegression is most accurate?
Select an answer to check.
Answer: Linear classifier.
Here, Linear classifier. is the right choice. Binary or multinomial. It aligns directly with what the question asks about which statement about logisticregression is most accurate. Competing choices sound plausible, but they miss the key condition.
Q52. How is LogisticRegression best characterized?
Select an answer to check.
Answer: Linear classifier.
In this case, Linear classifier. is correct. Binary or multinomial. It aligns directly with what the question asks about how is logisticregression best characterized. Competing choices sound plausible, but they miss the key condition.
Q53. Which option best describes DecisionTreeClassifier?
Select an answer to check.
Answer: Tree-based classifier.
The best option here is Tree-based classifier.. Interpretable but high variance. It aligns directly with what the question asks about which option best describes decisiontreeclassifier. Competing choices sound plausible, but they miss the key condition.
Q54. What is the primary purpose of DecisionTreeClassifier?
Select an answer to check.
Answer: Tree-based classifier.
For this question, Tree-based classifier. is correct. Interpretable but high variance. It aligns directly with what the question asks about what is the primary purpose of decisiontreeclassifier. Competing choices sound plausible, but they miss the key condition.
Q55. Which statement about DecisionTreeClassifier is most accurate?
Select an answer to check.
Answer: Tree-based classifier.
Tree-based classifier. is the correct answer here. Interpretable but high variance. It aligns directly with what the question asks about which statement about decisiontreeclassifier is most accurate. Competing choices sound plausible, but they miss the key condition.
Q56. How is DecisionTreeClassifier best characterized?
Select an answer to check.
Answer: Tree-based classifier.
Here, Tree-based classifier. is the right choice. Interpretable but high variance. This matches the core idea being tested around how is decisiontreeclassifier best characterized. Competing choices sound plausible, but they miss the key condition.
Q57. Which option best describes RandomForestClassifier?
Select an answer to check.
Answer: Ensemble of decision trees.
In this case, Ensemble of decision trees. is correct. Bagging-based. This matches the core idea being tested around which option best describes randomforestclassifier. Competing choices sound plausible, but they miss the key condition.
Q58. What is the primary purpose of RandomForestClassifier?
Select an answer to check.
Answer: Ensemble of decision trees.
The best option here is Ensemble of decision trees.. Bagging-based. This matches the core idea being tested around what is the primary purpose of randomforestclassifier. Competing choices sound plausible, but they miss the key condition.
Q59. Which statement about RandomForestClassifier is most accurate?
Select an answer to check.
Answer: Ensemble of decision trees.
For this question, Ensemble of decision trees. is correct. Bagging-based. This matches the core idea being tested around which statement about randomforestclassifier is most accurate. Competing choices sound plausible, but they miss the key condition.
Q60. How is RandomForestClassifier best characterized?
Select an answer to check.
Answer: Ensemble of decision trees.
Ensemble of decision trees. is the correct answer here. Bagging-based. This matches the core idea being tested around how is randomforestclassifier best characterized. Competing choices sound plausible, but they miss the key condition.
Q61. Which option best describes GBTClassifier?
Select an answer to check.
Answer: Gradient-Boosted Trees.
Here, Gradient-Boosted Trees. is the right choice. Boosting-based. That is exactly the concept behind which option best describes gbtclassifier in this context. Competing choices sound plausible, but they miss the key condition.
Q62. What is the primary purpose of GBTClassifier?
Select an answer to check.
Answer: Gradient-Boosted Trees.
In this case, Gradient-Boosted Trees. is correct. Boosting-based. That is exactly the concept behind what is the primary purpose of gbtclassifier in this context. Competing choices sound plausible, but they miss the key condition.
Q63. Which statement about GBTClassifier is most accurate?
Select an answer to check.
Answer: Gradient-Boosted Trees.
The best option here is Gradient-Boosted Trees.. Boosting-based. That is exactly the concept behind which statement about gbtclassifier is most accurate in this context. Competing choices sound plausible, but they miss the key condition.
Q64. How is GBTClassifier best characterized?
Select an answer to check.
Answer: Gradient-Boosted Trees.
For this question, Gradient-Boosted Trees. is correct. Boosting-based. That is exactly the concept behind how is gbtclassifier best characterized in this context. Competing choices sound plausible, but they miss the key condition.
Q65. Which option best describes LinearRegression?
Select an answer to check.
Answer: Linear model for regression.
Linear model for regression. is the correct answer here. Regularization options. That is exactly the concept behind which option best describes linearregression in this context. Competing choices sound plausible, but they miss the key condition.
Q66. What is the primary purpose of LinearRegression?
Select an answer to check.
Answer: Linear model for regression.
Here, Linear model for regression. is the right choice. Regularization options. It fits the requirement in the prompt about what is the primary purpose of linearregression. Competing choices sound plausible, but they miss the key condition.
Q67. Which statement about LinearRegression is most accurate?
Select an answer to check.
Answer: Linear model for regression.
In this case, Linear model for regression. is correct. Regularization options. It fits the requirement in the prompt about which statement about linearregression is most accurate. Competing choices sound plausible, but they miss the key condition.
Q68. How is LinearRegression best characterized?
Select an answer to check.
Answer: Linear model for regression.
The best option here is Linear model for regression.. Regularization options. It fits the requirement in the prompt about how is linearregression best characterized. Competing choices sound plausible, but they miss the key condition.
Q69. Which option best describes KMeans?
Select an answer to check.
Answer: Centroid-based clustering.
For this question, Centroid-based clustering. is correct. Distance-based. It fits the requirement in the prompt about which option best describes kmeans. Competing choices sound plausible, but they miss the key condition.
Q70. What is the primary purpose of KMeans?
Select an answer to check.
Answer: Centroid-based clustering.
Centroid-based clustering. is the correct answer here. Distance-based. It fits the requirement in the prompt about what is the primary purpose of kmeans. Competing choices sound plausible, but they miss the key condition.
Q71. Which statement about KMeans is most accurate?
Select an answer to check.
Answer: Centroid-based clustering.
Here, Centroid-based clustering. is the right choice. Distance-based. This is the most accurate statement for which statement about kmeans is most accurate. Competing choices sound plausible, but they miss the key condition.
Q72. How is KMeans best characterized?
Select an answer to check.
Answer: Centroid-based clustering.
In this case, Centroid-based clustering. is correct. Distance-based. This is the most accurate statement for how is kmeans best characterized. Competing choices sound plausible, but they miss the key condition.
Q73. Which option best describes ALS?
Select an answer to check.
Answer: Matrix factorization for collaborative filtering.
The best option here is Matrix factorization for collaborative filtering.. Implicit/explicit feedback. This is the most accurate statement for which option best describes als. Competing choices sound plausible, but they miss the key condition.
Q74. What is the primary purpose of ALS?
Select an answer to check.
Answer: Matrix factorization for collaborative filtering.
For this question, Matrix factorization for collaborative filtering. is correct. Implicit/explicit feedback. This is the most accurate statement for what is the primary purpose of als. Competing choices sound plausible, but they miss the key condition.
Q75. Which statement about ALS is most accurate?
Select an answer to check.
Answer: Matrix factorization for collaborative filtering.
Matrix factorization for collaborative filtering. is the correct answer here. Implicit/explicit feedback. This is the most accurate statement for which statement about als is most accurate. Competing choices sound plausible, but they miss the key condition.
Q76. How is ALS best characterized?
Select an answer to check.
Answer: Matrix factorization for collaborative filtering.
Here, Matrix factorization for collaborative filtering. is the right choice. Implicit/explicit feedback. It aligns directly with what the question asks about how is als best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q77. Which option best describes CrossValidator?
Select an answer to check.
Answer: K-fold CV for hyperparameter tuning.
In this case, K-fold CV for hyperparameter tuning. is correct. Param grid search. It aligns directly with what the question asks about which option best describes crossvalidator. The remaining choices fail because they don’t satisfy the full definition.
Q78. What is the primary purpose of CrossValidator?
Select an answer to check.
Answer: K-fold CV for hyperparameter tuning.
The best option here is K-fold CV for hyperparameter tuning.. Param grid search. It aligns directly with what the question asks about what is the primary purpose of crossvalidator. The remaining choices fail because they don’t satisfy the full definition.
Q79. Which statement about CrossValidator is most accurate?
Select an answer to check.
Answer: K-fold CV for hyperparameter tuning.
For this question, K-fold CV for hyperparameter tuning. is correct. Param grid search. It aligns directly with what the question asks about which statement about crossvalidator is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q80. How is CrossValidator best characterized?
Select an answer to check.
Answer: K-fold CV for hyperparameter tuning.
K-fold CV for hyperparameter tuning. is the correct answer here. Param grid search. It aligns directly with what the question asks about how is crossvalidator best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q81. Which option best describes TrainValidationSplit?
Select an answer to check.
Answer: Single train/validation split for tuning.
Here, Single train/validation split for tuning. is the right choice. Faster than CV; less robust. This matches the core idea being tested around which option best describes trainvalidationsplit. The remaining choices fail because they don’t satisfy the full definition.
Q82. What is the primary purpose of TrainValidationSplit?
Select an answer to check.
Answer: Single train/validation split for tuning.
In this case, Single train/validation split for tuning. is correct. Faster than CV; less robust. This matches the core idea being tested around what is the primary purpose of trainvalidationsplit. The remaining choices fail because they don’t satisfy the full definition.
Q83. Which statement about TrainValidationSplit is most accurate?
Select an answer to check.
Answer: Single train/validation split for tuning.
The best option here is Single train/validation split for tuning.. Faster than CV; less robust. This matches the core idea being tested around which statement about trainvalidationsplit is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q84. How is TrainValidationSplit best characterized?
Select an answer to check.
Answer: Single train/validation split for tuning.
For this question, Single train/validation split for tuning. is correct. Faster than CV; less robust. This matches the core idea being tested around how is trainvalidationsplit best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q85. Which option best describes ParamGrid?
Select an answer to check.
Answer: Grid of hyperparameters for tuning.
Grid of hyperparameters for tuning. is the correct answer here. Used with CV/TVS. This matches the core idea being tested around which option best describes paramgrid. The remaining choices fail because they don’t satisfy the full definition.
Q86. What is the primary purpose of ParamGrid?
Select an answer to check.
Answer: Grid of hyperparameters for tuning.
Here, Grid of hyperparameters for tuning. is the right choice. Used with CV/TVS. That is exactly the concept behind what is the primary purpose of paramgrid in this context. The remaining choices fail because they don’t satisfy the full definition.
Q87. Which statement about ParamGrid is most accurate?
Select an answer to check.
Answer: Grid of hyperparameters for tuning.
In this case, Grid of hyperparameters for tuning. is correct. Used with CV/TVS. That is exactly the concept behind which statement about paramgrid is most accurate in this context. The remaining choices fail because they don’t satisfy the full definition.
Q88. How is ParamGrid best characterized?
Select an answer to check.
Answer: Grid of hyperparameters for tuning.
The best option here is Grid of hyperparameters for tuning.. Used with CV/TVS. That is exactly the concept behind how is paramgrid best characterized in this context. The remaining choices fail because they don’t satisfy the full definition.
Q89. Which option best describes Evaluators?
Select an answer to check.
Answer: Metrics like AUC, accuracy, RMSE.
For this question, Metrics like AUC, accuracy, RMSE. is correct. BinaryClassification/Regression Evaluators. That is exactly the concept behind which option best describes evaluators in this context. The remaining choices fail because they don’t satisfy the full definition.
Q90. What is the primary purpose of Evaluators?
Select an answer to check.
Answer: Metrics like AUC, accuracy, RMSE.
Metrics like AUC, accuracy, RMSE. is the correct answer here. BinaryClassification/Regression Evaluators. That is exactly the concept behind what is the primary purpose of evaluators in this context. The remaining choices fail because they don’t satisfy the full definition.
Q91. Which statement about Evaluators is most accurate?
Select an answer to check.
Answer: Metrics like AUC, accuracy, RMSE.
Here, Metrics like AUC, accuracy, RMSE. is the right choice. BinaryClassification/Regression Evaluators. It fits the requirement in the prompt about which statement about evaluators is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q92. How is Evaluators best characterized?
Select an answer to check.
Answer: Metrics like AUC, accuracy, RMSE.
In this case, Metrics like AUC, accuracy, RMSE. is correct. BinaryClassification/Regression Evaluators. It fits the requirement in the prompt about how is evaluators best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q93. Which option best describes PipelineModel persistence?
Select an answer to check.
Answer: Save/load fitted pipelines.
The best option here is Save/load fitted pipelines.. Model.save / load. It fits the requirement in the prompt about which option best describes pipelinemodel persistence. The remaining choices fail because they don’t satisfy the full definition.
Q94. What is the primary purpose of PipelineModel persistence?
Select an answer to check.
Answer: Save/load fitted pipelines.
For this question, Save/load fitted pipelines. is correct. Model.save / load. It fits the requirement in the prompt about what is the primary purpose of pipelinemodel persistence. The remaining choices fail because they don’t satisfy the full definition.
Q95. Which statement about PipelineModel persistence is most accurate?
Select an answer to check.
Answer: Save/load fitted pipelines.
Save/load fitted pipelines. is the correct answer here. Model.save / load. It fits the requirement in the prompt about which statement about pipelinemodel persistence is most accurate. The remaining choices fail because they don’t satisfy the full definition.
Q96. How is PipelineModel persistence best characterized?
Select an answer to check.
Answer: Save/load fitted pipelines.
Here, Save/load fitted pipelines. is the right choice. Model.save / load. This is the most accurate statement for how is pipelinemodel persistence best characterized. The remaining choices fail because they don’t satisfy the full definition.
Q97. Which option best describes MLlib vs scikit-learn?
Select an answer to check.
Answer: Distributed vs single-node.
In this case, Distributed vs single-node. is correct. Choose based on data size. This is the most accurate statement for which option best describes mllib vs scikit-learn. The remaining choices fail because they don’t satisfy the full definition.
Q98. What is the primary purpose of MLlib vs scikit-learn?
Select an answer to check.
Answer: Distributed vs single-node.
The best option here is Distributed vs single-node.. Choose based on data size. This is the most accurate statement for what is the primary purpose of mllib vs. The remaining choices fail because they don’t satisfy the full definition.
Q99. Which statement about MLlib vs scikit-learn is most accurate?
Select an answer to check.
Answer: Distributed vs single-node.
For this question, Distributed vs single-node. is correct. Choose based on data size. This is the most accurate statement for which statement about mllib vs scikit-learn is most. The remaining choices fail because they don’t satisfy the full definition.
Q100. How is MLlib vs scikit-learn best characterized?
Select an answer to check.
Answer: Distributed vs single-node.
Distributed vs single-node. is the correct answer here. Choose based on data size. This is the most accurate statement for how is mllib vs scikit-learn best characterized. The remaining choices fail because they don’t satisfy the full definition.