MCQs

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Overall MCQs for Reference

What does productionization in machine learning primarily refer to?
a. Model training
b. Model development
c. Deploying models for real-world use
d. Data collection
Answer: cml-ops+1
Which phase is not typically part of production ML lifecycle?
a. Experimentation
b. Model serving
c. Data labelling
d. Continuous monitoring
Answer: cmachinelearningmastery+1
In MLOps, which process allows consistent and automated model deployment?
a. Manual updates
b. A/B testing
c. Continuous Integration/Continuous Delivery (CI/CD)
d. Transfer Learning
Answer: cml-ops+1
Why is data drift important to monitor in production?
a. It affects code structure
b. It impacts model latency
c. It may degrade model performance
d. It increases training time
Answer: cevidentlyai+1
What is model retraining in production systems?
a. Training on test data
b. Re-training existing models with new data
c. Testing model code
d. Building new datasets
Answer: bevidentlyai+1
What is the main benefit of automated data pipelines in production ML?
a. Lower storage costs
b. Manual intervention
c. Improved reproducibility and scalability
d. Static data updates
Answer: cmachinelearningmastery+1
Which is a common method to expose a machine learning model in production?
a. Jupyter notebook
b. REST API
c. CSV file
d. PowerPoint presentation
Answer: bmachinelearningmastery+1
In production ML, rollback strategies are essential because:
a. They reduce model complexity
b. They allow reverting to stable versions after failures
c. They increase data volume
d. They improve hyperparameter tuning
Answer: bml-ops+1
What purpose does monitoring serve for a deployed ML model?
a. Checking code style
b. Ensuring prediction quality and system health
c. Model pre-processing
d. Retrieving old datasets
Answer: bevidentlyai+1
What is model versioning in ML production pipelines?
a. Changing hyperparameters only
b. Keeping records of model code changes
c. Tracking every deployed model version
d. Data shuffling for training
Answer: cml-ops+1
Which standard helps with automated model retraining and deployments?
a. Agile
b. CRISP-DM
c. CI/CD
d. CMMI
Answer: cmachinelearningmastery+1
Model monitoring typically checks all except:
a. Data drift
b. Model accuracy
c. Code syntax errors
d. Prediction skew
Answer: cevidentlyai+1
What is the function of a feature store in ML production?
a. Visualization
b. Serving and reusing consistent features
c. Reducing computation time
d. Manual annotation
Answer: bmachinelearningmastery+1
Which of these is most closely related to model reproducibility?
a. Feature store versioning
b. Changing data schemas
c. Model latency reduction
d. Graphics processing
Answer: aml-ops+1
A shadow deployment refers to:
a. Serving predictions to end-users only
b. Running a new model in parallel without affecting users
c. Model output encryption
d. Compressing model files
Answer: bevidentlyai+1
Why is monitoring system resource usage important for production ML?
a. Billing users correctly
b. Detecting performance bottlenecks and scaling needs
c. Generating synthetic data
d. Improving UI design
Answer: bmachinelearningmastery+1
What is blue-green deployment?
a. Running models on two types of hardware
b. Switching traffic between old and new models with minimal downtime
c. Using blue color for logs
d. Encrypting user data in green containers
Answer: bevidentlyai+1
Continuous monitoring of ML models means:
a. Monitoring code once
b. Monitoring input data, predictions, and errors over time
c. Deleting old logs
d. Analyzing model hyperparameters
Answer: bml-ops+1
What is the main reason to automate retraining in production ML?
a. Save storage
b. Adapt model to changing data patterns
c. Reduce prediction cost
d. Increase batch size
Answer: bmachinelearningmastery+1
Which concept describes when a deployed model’s performance drops on new unseen data?
a. Overfitting
b. Underfitting
c. Model drift
d. Data augmentation
Answer: cevidentlyai+1
ML model explainability tools in production help:
a. Increase prediction speed
b. Understand and validate model behavior
c. Hide model logic
d. Reduce hardware costs
Answer: bmachinelearningmastery+1
Data pipeline orchestration tools mainly support:
a. Version control
b. Automating and managing workflow dependencies
c. GPU allocation
d. Feature annotation
Answer: bml-ops+1
Real-time model serving typically requires:
a. Batch jobs
b. Low latency and high throughput systems
c. Retraining
d. Manual feature engineering
Answer: bevidentlyai+1
Canary deployment is a strategy that:
a. Randomly deletes models
b. Gradually rolls out changes to a subset of users
c. Encrypts model logs
d. Adds artificial noise
Answer: bmachinelearningmastery+1
Audit logging in ML production is important for:
a. Model training speed
b. Regulatory compliance and debugging
c. Data visualization
d. Metrics aggregation
Answer: bml-ops+1
What is inference latency?
a. Time taken for model development
b. Time taken for a model to generate prediction
c. Time spent in feature engineering
d. Training duration
Answer: bevidentlyai+1
Offline batch scoring is best suited for:
a. Predicting in near real-time
b. Large-scale prediction tasks scheduled at intervals
c. Hyperparameter tuning
d. Image processing
Answer: bmachinelearningmastery+1
Robustness tests for production ML models evaluate:
a. Model accuracy only
b. Performance under edge cases and adversarial inputs
c. Data preprocessing speed
d. Feature scaling technique
Answer: bevidentlyai+1
Which is a common testing strategy before production rollout?
a. Model ensembling
b. A/B testing
c. Indexing databases
d. Stochastic optimization
Answer: bmachinelearningmastery+1
What is a model registry used for?
a. Storing user data
b. Tracking, storing, and managing model versions
c. Generating synthetic data
d. Hyperparameter optimization
Answer: bml-ops+1
SLAs (Service Level Agreements) in ML production typically define:
a. Coding conventions
b. Maximum expected latency or uptime for predictions
c. Feature naming
d. Number of annotations
Answer: bml-ops+1
In cloud ML production systems, auto-scaling refers to:
a. Increasing model accuracy automatically
b. Adjusting computing resources in response to load
c. Random data resampling
d. Feature hashing
Answer: bml-ops+1
Which of the following ensures data security in ML production?
a. Model as a Service
b. Data encryption and access control
c. Hyperparameter optimization
d. Batch normalization
Answer: bml-ops+1
What is “concept drift” in deployed ML systems?
a. Data format change
b. Change in statistical properties of the target variable
c. ML model code update
d. Feature renaming
Answer: bevidentlyai+1
Data validation in production ML:
a. Is skipped for high-performance systems
b. Helps detect schema or distribution changes early
c. Is only for model training
d. Affects visualization only
Answer: bmachinelearningmastery+1
Containerization assists ML production because:
a. It slows down deployment
b. It isolates dependencies and improves reproducibility
c. It requires specialized OS
d. It increases data drift
Answer: bml-ops+1
Blue/green deployment helps with:
a. Feature scaling
b. Zero-downtime model updates
c. Output serialization
d. Log anonymization
Answer: bevidentlyai+1
Continuous training (CT) in ML production involves:
a. Ignoring new data
b. Frequent retraining with new production data
c. Changing ML frameworks
d. Batch prediction only
Answer: bmachinelearningmastery+1
Which of these helps prevent feature drift?
a. Model hyperparameter tuning
b. Feature store and data validation scripts
c. Logging model errors
d. Output anonymization
Answer: bml-ops+1
ML model fairness checks in production are used to:
a. Increase cost
b. Ensure model predictions do not exhibit bias
c. Speed up inference
d. Aggregate features
Answer: bevidentlyai+1
Service orchestration frameworks (e.g., Airflow, Kubeflow) enable:
a. Scheduling and automating ML pipelines
b. Data visualization
c. Model serialization
d. Reducing feature space
Answer: amachinelearningmastery+1
For high-availability in ML prediction services, use:
a. Feature hashing
b. Load balancing and redundancy
c. Model pruning
d. Bagging
Answer: bml-ops+1
Which of these tests is used to validate production code endpoints?
a. Focus group
b. Endpoint smoke testing
c. Feature hashing
d. Principal component analysis
Answer: bevidentlyai+1
Model canarying is best described as:
a. Running end-to-end tests
b. Gradually exposing a new model to a small % of production traffic
c. Heavy batch processing
d. Feature scrambling
Answer: bmachinelearningmastery+1
What does inference logging help track?
a. Model training duration
b. Input data, output predictions, and any errors
c. Only aggregate metrics
d. Version control only
Answer: bml-ops+1
MLOps is mostly concerned with:
a. Manual model building
b. End-to-end lifecycle operations from development to deployment and monitoring
c. Model visualization
d. Label propagation
Answer: bmachinelearningmastery+1
Anti-pattern for model productionization includes:
a. Automating deployments
b. Manual updates without version control
c. Automated monitoring
d. Robust CI/CD systems
Answer: bml-ops+1
Data lineage supports:
a. Debugging, reproducibility and compliance tracking in production ML
b. Batch feature scaling
c. Model parallelism
d. GPU resource sharing
Answer: amachinelearningmastery+1
Error budgets in ML production mean:
a. The maximum number of allowable model errors before rollout action is taken
b. Random cost calculation
c. Number of training features
d. Time needed for training
Answer: aml-ops+1
What does shift-left testing mean for ML systems?
a. Post-production validation only
b. Early integration of testing in development lifecycle
c. Running tests on end-user devices
d. Testing after failure
Answer: bmachinelearningmastery+1

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