MCQs

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Overall MCQs for Reference

1. Which of the following best describes a machine learning pipeline?
   a. A linear sequence of unrelated models
   b. A discrete component for data visualization
   c. A set of automated steps for end-to-end model building
   d. A single algorithmic model
   Answer: c. A set of automated steps for end-to-end model buildinggeeksforgeeks+2​
2. What is generally the first step in a typical machine learning pipeline?
   a. Model deployment
   b. Data collection/preprocessing
   c. Feature scaling
   d. Cross-validation
   Answer: b. Data collection/preprocessingdatacamp+1​
3. Feature engineering occurs in which stage?
   a. After training the model
   b. Before data collection
   c. Before splitting the dataset
   d. After data preprocessing
   Answer: d. After data preprocessinggeeksforgeeks​
4. Which step divides the dataset into training and testing sets?
   a. Data preprocessing
   b. Feature extraction
   c. Data splitting
   d. Model deployment
   Answer: c. Data splittingdatacamp+1​
5. What is the main purpose of automating a machine learning pipeline?
   a. To reduce data quality
   b. To increase manual intervention
   c. To reduce errors and improve efficiency
   d. To prevent model validation
   Answer: c. To reduce errors and improve efficiencydevelopers.google+2​
6. Which library is commonly used for building pipelines in Python?
   a. pandas
   b. matplotlib
   c. scikit-learn
   d. seaborn
   Answer: c. scikit-learndatacamp​
7. What is the function of the ‘fit’ method in a pipeline?
   a. To train the pipeline steps sequentially
   b. To visualize accuracy
   c. To validate the input data
   d. To export the pipeline to cloud
   Answer: a. To train the pipeline steps sequentiallydatacamp​
8. What stage of the pipeline focuses on improving the model’s performance by altering input variables?
   a. Data cleaning
   b. Feature engineering
   c. Model selection
   d. Deployment
   Answer: b. Feature engineeringgeeksforgeeks+1​
9. Which is NOT a typical part of a machine learning pipeline?
   a. Data preprocessing
   b. Feature engineering
   c. Data encryption
   d. Model evaluation
   Answer: c. Data encryptiongeeksforgeeks+1​
10. What technique is often used in the model evaluation stage to avoid overfitting?
    a. Data scaling
    b. Cross-validation
    c. Data augmentation
    d. Model stacking
    Answer: b. Cross-validationgeeksforgeeks+1​
11. What is the main advantage of using a pipeline for model deployment?
    a. It allows direct modification of training data
    b. It fully automates data transformations and predictions
    c. It supports only unsupervised learning
    d. It prevents monitoring of models
    Answer: b. It fully automates data transformations and predictionsibm+1​
12. Which step should happen before model selection?
    a. Model testing
    b. Feature creation and selection
    c. Model retraining
    d. Model deployment
    Answer: b. Feature creation and selectiondatacamp+1​
13. Which of the following is necessary for pipeline reproducibility?
    a. Randomized steps without seed
    b. Version controlling the pipeline
    c. Multiple concurrent deployments
    d. Dynamic schema evolution
    Answer: b. Version controlling the pipelinedevelopers.google+1​
14. What is a ‘transform’ method used for in a ML pipeline?
    a. To retrain the pipeline
    b. To process new data using learned parameters
    c. To monitor resource usage
    d. To split the pipeline into multiple models
    Answer: b. To process new data using learned parametersdatacamp​
15. A pipeline run that repeats model updates on a schedule is called:
    a. On-prem training
    b. Continuous pipeline
    c. Batch processing
    d. Model stacking
    Answer: b. Continuous pipelineibm+1​
16. A key benefit of modular pipeline components is:
    a. Inflexibility
    b. Independent development
    c. Increased coupling
    d. Hardcoded features
    Answer: b. Independent developmentlearn.microsoft+1​
17. Model performance in a pipeline is generally checked in which phase?
    a. Data collection
    b. Model training
    c. Model evaluation
    d. Logging
    Answer: c. Model evaluationgeeksforgeeks+1​
18. Which component manages model retraining when new data becomes available?
    a. Data splitter
    b. Model deployment
    c. Validation pipeline
    d. Continuous integration
    Answer: c. Validation pipelinelearn.microsoft+1​
19. What is the output of the feature engineering step generally called?
    a. Raw data
    b. Feature matrix
    c. Evaluation score
    d. Parameter values
    Answer: b. Feature matrixgeeksforgeeks+1​
20. Which format is often used to persist a trained ML pipeline?
    a. JPG
    b. PNG
    c. Pickle/Joblib
    d. CSV
    Answer: c. Pickle/Joblibdatacamp​
21. What does a data preprocessing step in the pipeline usually do?
    a. Trains the model
    b. Cleans and normalizes input data
    c. Inputs model predictions
    d. Deploys the model
    Answer: b. Cleans and normalizes input datageeksforgeeks+1​
22. Pipelines help with which aspect of MLOps?
    a. Only feature selection
    b. Automation and reproducibility
    c. Manual evaluation
    d. Increased data leakage
    Answer: b. Automation and reproducibilitydevelopers.google+3​
23. Which measure is NOT typically used for pipeline model evaluation?
    a. Precision
    b. Recall
    c. F1-score
    d. Data encryption time
    Answer: d. Data encryption timescribd+1​
24. Tuning hyperparameters is part of which pipeline step?
    a. Data collection
    b. Model optimization
    c. Logging
    d. Data storage
    Answer: b. Model optimizationgeeksforgeeks​
25. Automated monitoring in pipelines primarily checks for:
    a. Data drift and model accuracy
    b. Increased encryption
    c. Novelty detection only
    d. Export format
    Answer: a. Data drift and model accuracylearn.microsoft+2​
26. Which step comes immediately after model training in a standard pipeline?
    a. Model evaluation
    b. Data labeling
    c. Feature selection
    d. Data cleaning
    Answer: a. Model evaluationdatacamp+1​
27. Pipelines are especially useful for:
    a. Handling only small datasets
    b. Standardizing and reusing workflows
    c. Ad-hoc analysis
    d. Bypassing feature engineering
    Answer: b. Standardizing and reusing workflowsibm+1​
28. The process of automating data extraction from APIs fits in which step?
    a. Model optimization
    b. Data collection
    c. Model testing
    d. Feature engineering
    Answer: b. Data collectiongeeksforgeeks​
29. What technique often precedes model deployment in the pipeline?
    a. Model versioning
    b. Feature scaling
    c. Data splitting
    d. Model stacking
    Answer: a. Model versioningdevelopers.google+1​
30. A major advantage of using pipelines for model deployment is:
    a. Easier rollbacks
    b. Model staleness
    c. Model randomness
    d. Data loss
    Answer: a. Easier rollbacksibm+1​
31. What is the role of the validation set in a pipeline?
    a. For feature engineering
    b. For hyperparameter tuning
    c. For production inference
    d. For raw prediction
    Answer: b. For hyperparameter tuninggeeksforgeeks​
32. Pipelines help avoid ‘data leakage’ by:
    a. Automating deployment
    b. Isolating training data from unseen test data
    c. Increasing randomization
    d. Skipping feature selection
    Answer: b. Isolating training data from unseen test datadevelopers.google+1​
33. MLflow and Kubeflow are examples of:
    a. Model scoring tools
    b. MLOps pipeline orchestration tools
    c. Visualization frameworks
    d. Online learning
    Answer: b. MLOps pipeline orchestration toolsgeeksforgeeks​
34. The term “serving pipeline” refers to:
    a. Data labeling workflow
    b. Production prediction workflow
    c. Feature engineering
    d. Feature scaling
    Answer: b. Production prediction workflowlearn.microsoft+1​
35. Scheduling pipeline runs provides:
    a. Model reproducibility
    b. Only one model version
    c. Pipeline obsolescence
    d. Model stacking
    Answer: a. Model reproducibilitydevelopers.google​
36. Which cloud platform provides pipeline support for ML as a managed service?
    a. TensorFlow only
    b. Amazon SageMaker
    c. Excel
    d. Android
    Answer: b. Amazon SageMakerkoenig-solutions+1​
37. Building a pipeline by chaining “Transformers” and “Estimators” refers to which library?
    a. scikit-learn
    b. TensorFlow
    c. Spark ML
    d. XGBoost
    Answer: c. Spark MLspark.apache​
38. In an ML pipeline, “data drift” means:
    a. Change in training code
    b. Change in incoming data patterns over time
    c. Network interruptions
    d. Model accuracy improvement only
    Answer: b. Change in incoming data patterns over timelearn.microsoft+1​
39. The process of verifying that the pipeline produces similar results over time is called:
    a. Replication
    b. Reproducibility
    c. Cross-validation
    d. Feature selection
    Answer: b. Reproducibilitygeeksforgeeks​
40. Which of the following is NOT a benefit of using pipelines?
    a. Reduced manual errors
    b. Consistent workflows
    c. Lack of scalability
    d. Streamlined processes
    Answer: c. Lack of scalabilityibm+1​
41. A pipeline that triggers retraining when accuracy drops is said to use:
    a. Static scheduling
    b. Event-driven automation
    c. Random implementation
    d. Manual updates
    Answer: b. Event-driven automationdevelopers.google​
42. Which data preprocessing task removes irrelevant features in a pipeline?
    a. Feature selection
    b. Data scaling
    c. Data cleaning
    d. Model stacking
    Answer: a. Feature selectiongeeksforgeeks​
43. What does “end-to-end” refer to in ML pipelines?
    a. Only model training
    b. Full lifecycle from raw data to deployment
    c. Data visualization only
    d. Metrics monitoring
    Answer: b. Full lifecycle from raw data to deploymentibm+2​
44. The concept of “pipeline orchestration” is associated with:
    a. Single script workflows
    b. Managing and scheduling multiple pipeline processes
    c. Manual model evaluation
    d. Model stacking
    Answer: b. Managing and scheduling multiple pipeline processeslearn.microsoft+3​
45. In cross-validation within a pipeline, the main aim is:
    a. To maximize training error
    b. To assess generalizability
    c. To deploy the model
    d. To drop features
    Answer: b. To assess generalizabilitygeeksforgeeks​
46. Which pipeline stage is likely to include transforming categorical variables to numeric?
    a. Model selection
    b. Feature engineering
    c. Model testing
    d. Data collection
    Answer: b. Feature engineeringgeeksforgeeks​
47. A pipeline’s “transformers” are:
    a. Functions for visualizing data
    b. Steps to apply transformations to data
    c. Pipelines inside pipelines
    d. Pipeline validation scripts
    Answer: b. Steps to apply transformations to dataspark.apache​
48. “Chaining” in an ML pipeline means:
    a. Random order of steps
    b. Connecting stages to form a sequence
    c. Visualizing model graphs
    d. Only running evaluation
    Answer: b. Connecting stages to form a sequencespark.apache+1​
49. When is an ML pipeline considered “production-ready”?
    a. All steps are manual
    b. Model can automatically retrain, monitor, and serve predictions
    c. Only data cleaning is implemented
    d. Only model training is implemented
    Answer: b. Model can automatically retrain, monitor, and serve predictionslearn.microsoft+1​
50. What enables a pipeline to handle multiple models efficiently?
    a. Manual evaluation
    b. Multi-step orchestration
    c. Random sampling
    d. Factor analysis
    Answer: b. Multi-step orchestrationlearn.microsoft​

1. https://www.geeksforgeeks.org/blogs/machine-learning-pipeline/
2. https://www.datacamp.com/tutorial/tutorial-machine-learning-pipelines-mlops-deployment
3. https://www.ibm.com/think/topics/machine-learning-pipeline
4. https://developers.google.com/machine-learning/managing-ml-projects/pipelines
5. https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2
6. https://www.scribd.com/document/878023509/Machine-Learning-Mcq
7. https://www.koenig-solutions.com/the-machine-learning-pipeline-on-aws-quiz-questions-answers
8. https://spark.apache.org/docs/latest/ml-pipeline.html
9. https://www.matillion.com/blog/what-are-machine-learning-pipelines
10. https://www.interviewbit.com/machine-learning-mcq/
11. https://cloudxlab.com/assessment/displayslide/7628/pipeline-mcq
12. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
13. https://wayground.com/admin/quiz/5fd02a2b2b8665001e3fc48b/machine-learning-pipeline
14. https://testbook.com/objective-questions/mcq-on-instruction-pipelining–5eea6a0a39140f30f369dae9
15. https://www.geeksforgeeks.org/quizzes/pipelining-and-addressing-modes/
16. https://testbook.com/objective-questions/mcq-on-instruction-pipeline–5eea6a0a39140f30f369daee
17. https://www.scribd.com/document/512977404/MCQ-of-Machine-Learning
18. https://technicalblog.in/languagewise-mcqs/machine-learning-mcqs/
19. https://testbook.com/objective-questions/mcq-on-pipelining-hazards–5eea6a0a39140f30f369db10
20. https://www.sanfoundry.com/computer-organization-mcqs-pipelining/