MCQs

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Overall MCQs for Reference

In ML deployment, which strategy gradually exposes a new model to a small user group before full rollout?
a. Shadow Deployment
b. Canary Deployment
c. Blue-Green Deployment
d. Batch Deployment
Answer: b. Canary Deploymentgeeksforgeeks+1
What is the primary benefit of containerizing ML models for deployment?
a. Increases security
b. Ensures reproducibility across environments
c. Reduces training time
d. Improves prediction accuracy
Answer: b. Ensures reproducibility across environmentsgrowexx+1
Which cloud platform is widely used for deploying scalable ML models?
a. Heroku
b. AWS
c. Hadoop
d. Apache Pig
Answer: b. AWSaws.amazon+1
What role does version control play in ML model deployment?
a. Enhances algorithm quality
b. Tracks and manages model changes
c. Reduces computational cost
d. Automates hyperparameter tuning
Answer: b. Tracks and manages model changesultralytics+1
Which deployment method runs the new model alongside the production model but doesn’t affect user traffic?
a. Blue-Green
b. Shadow Deployment
c. Canary Deployment
d. Rollback Deployment
Answer: b. Shadow Deploymentgeeksforgeeks+1
When testing ML models before deployment, which is the most crucial dataset?
a. Test Set
b. Training Set
c. Validation Set
d. Augmented Set
Answer: a. Test Setexploredatabase+1
What is the primary advantage of using edge computing for ML inference?
a. Increases network latency
b. Reduces cloud dependence
c. Requires high memory resources
d. Increases batch processing speed
Answer: b. Reduces cloud dependencegrowexx+1
Which MLOps tool is primarily used for orchestration and automation in ML deployments?
a. Hadoop
b. Kubernetes
c. RapidMiner
d. Tableau
Answer: b. Kubernetesscmgalaxy+2
What is the purpose of automated monitoring in ML deployments?
a. To increase bandwidth usage
b. To track model performance and drift
c. To decrease error logging
d. To enhance CPU speed
Answer: b. To track model performance and driftultralytics+1
What does the rollback feature in deployment strategies enable?
a. Enhances GPU performance
b. Reverts to a previous model version after failure
c. Accelerates real-time inference
d. Reduces storage requirements
Answer: b. Reverts to a previous model version after failureaws.amazon+1
Which type of deployment is best for real-time, low-latency applications?
a. Batch Deployment
b. Edge Deployment
c. Cloud Deployment
d. Hybrid Deployment
Answer: b. Edge Deploymentgrowexx+1
Which of the following is NOT a common ML model deployment environment?
a. Cloud
b. Local Server
c. Mobile Device
d. Quantum Computer
Answer: d. Quantum Computerultralytics+1
What is the function of APIs in ML model deployment?
a. Facilitate direct data processing
b. Enable model access through web services
c. Increase database size
d. Reduce programming errors
Answer: b. Enable model access through web servicesgrowexx+1
Which deployment strategy allows rapid rollback if the new model fails?
a. Shadow Deployment
b. Canary Deployment
c. Blue-Green Deployment
d. Monolithic Deployment
Answer: c. Blue-Green Deploymentultralytics+1
What is a key advantage of using Docker for ML deployment?
a. Faster neural network convergence
b. Consistent environments for development and production
c. Higher signal propagation speed
d. Reduced data preprocessing
Answer: b. Consistent environments for development and productiongrowexx+1
What is the main reason for model drift in production environments?
a. Training errors
b. Changes in input data distribution
c. Increased model complexity
d. Enhanced hardware specifications
Answer: b. Changes in input data distributiongeeksforgeeks+1
How does model monitoring contribute to ML deployment success?
a. Detects unexpected performance degradation
b. Increases CPU bandwidth
c. Reduces redundancy
d. Improves data normalization
Answer: a. Detects unexpected performance degradationultralytics+1
Which ML deployment practice helps maintain reproducibility?
a. Model quantization
b. Containerization
c. Random initialization
d. Data augmentation
Answer: b. Containerizationgrowexx+1
What is a typical challenge for deploying ML models in IoT devices?
a. Abundant memory
b. Limited computational resources
c. Unlimited bandwidth
d. Frequent cloud updates
Answer: b. Limited computational resourcesultralytics+1
Which tool is NOT commonly used for ML model deployment?
a. Docker
b. Kubernetes
c. TensorFlow
d. MS Paint
Answer: d. MS Paintscmgalaxy+1
Which deployment model provides maximum security for sensitive ML applications?
a. Public Cloud
b. Private Cloud
c. Multi-cloud
d. Community Cloud
Answer: b. Private Cloudscribd+1
Which step is crucial before deploying a model to production?
a. Feature scaling
b. Model validation with test data
c. Data randomization
d. Hyperparameter search
Answer: b. Model validation with test dataexploredatabase+1
What does “model packaging” refer to in deployments?
a. Wrapping models with input/output interfaces
b. Optimizing model weights
c. Reducing algorithm complexity
d. Increasing RAM usage
Answer: a. Wrapping models with input/output interfacesgrowexx+1
What is the main function of a CI/CD pipeline in ML deployment?
a. Speeding up model training
b. Automating tests and deployments
c. Increasing the number of neurons
d. Reducing sample entropy
Answer: b. Automating tests and deploymentsultralytics+1
Which deployment strategy reduces business risk by minimizing downtime?
a. Hot Swapping
b. Blue-Green Deployment
c. Batch Transformation Deployment
d. Static Deployment
Answer: b. Blue-Green Deploymentaws.amazon+1
What is a “deployment endpoint” in ML?
a. Location where training occurs
b. Interface for serving predictions to users
c. Database for storing raw data
d. Model weight archive
Answer: b. Interface for serving predictions to usersaws.amazon+1
What can be used to optimize ML models before deployment for resource-constrained settings?
a. Dropout layers
b. Quantization and pruning
c. Overfitting
d. Batch normalization
Answer: b. Quantization and pruninggrowexx+1
What is the major advantage of cloud deployment for ML models?
a. Reduced scalability
b. Increased cost
c. On-demand resource allocation
d. Slow inference
Answer: c. On-demand resource allocationultralytics+1
Deploying an ML model on-premise primarily benefits organizations by?
a. Lowering latency and increasing data privacy
b. Increasing network cloud costs
c. Improving overfitting rates
d. Reducing workflow automation
Answer: a. Lowering latency and increasing data privacygrowexx+1
Which best describes “model serving” in ML deployments?
a. The data cleaning process
b. Providing predictions via an interface
c. Feature engineering
d. Model retraining
Answer: b. Providing predictions via an interfaceultralytics+1
A major challenge in ML deployment for mobile applications is:
a. High memory and CPU limitations
b. Unlimited cloud storage
c. Frequent architecture changes
d. Slow network connections
Answer: a. High memory and CPU limitationsgrowexx+1
How can organizations prevent unauthorized access to deployed ML models?
a. Disable input validation
b. Use authentication and encryption
c. Open all API ports
d. Reduce model layers
Answer: b. Use authentication and encryptionultralytics+1
What ensures repeatable results during ML deployment?
a. Deterministic pipelines and environment standardization
b. Randomized data processing
c. Parallel model training
d. Frequent hyperparameter tuning
Answer: a. Deterministic pipelines and environment standardizationgrowexx+1
What is a common method for scalable ML deployment in the cloud?
a. Using microservices architecture
b. Increasing batch size
c. Model ensembling
d. Synchronous Python scripts
Answer: a. Using microservices architectureultralytics+1
Which is NOT a best practice for deploying ML models?
a. Robust monitoring and logging
b. Containerization
c. Ignoring data drift
d. Automated testing
Answer: c. Ignoring data driftgrowexx+1
What does model retraining in production enable?
a. Continuous learning from new data
b. Frequent API changes
c. Increased memory usage
d. Reduced deployment security
Answer: a. Continuous learning from new dataultralytics+1
For scaling prediction services, what is typically employed?
a. API rate limiting
b. Load balancing
c. Static resource allocation
d. Deep feature selection
Answer: b. Load balancinggrowexx+1
What’s essential for detecting concept drift in deployed ML models?
a. Regular performance evaluation on new data
b. Ignoring feedback loops
c. Maximizing GPU allocation
d. Frequent retraining only
Answer: a. Regular performance evaluation on new dataultralytics+1
Which technique helps avoid downtime while deploying a new model version?
a. Blue-Green Deployment
b. Monolithic updates
c. Recursive feature elimination
d. Random restarts
Answer: a. Blue-Green Deploymentgrowexx+1
What is the main purpose of scaling replica pods in Kubernetes ML deployments?
a. Ability to handle increased user requests
b. Boost data quality
c. Enhance decision trees
d. Reduce model bias
Answer: a. Ability to handle increased user requestsmachinelearningmastery+1
Selecting an appropriate deployment infrastructure mainly depends on:
a. Model’s computational and business requirements
b. Experiment history
c. Training algorithm
d. Number of layers
Answer: a. Model’s computational and business requirementsultralytics+1
How do organizations typically monitor model drift post deployment?
a. By tracking input and output distributions
b. Ignoring feedback
c. Data randomization
d. Manual logs only
Answer: a. By tracking input and output distributionsgrowexx+1
What should be included in a deployment pipeline for ML projects?
a. Testing, validation, and monitoring steps
b. Random guessing
c. Unsupervised data selection
d. Manual script runs
Answer: a. Testing, validation, and monitoring stepsultralytics+1
What is the recommended format for saving a trained scikit-learn model for deployment?
a. Pickle/joblib
b. PNG
c. CSV
d. PDF
Answer: a. Pickle/joblibmachinelearningmastery+1
Which factor is critical when choosing a deployment method for ML models?
a. Scalability needs and latency requirements
b. Number of neurons
c. Activation function type
d. Training set size
Answer: a. Scalability needs and latency requirementsgrowexx+1
Which deployment scenario requires real-time streaming of data and predictions?
a. Batch deployment
b. Streaming deployment
c. Monolithic deployment
d. Cross-validation deployment
Answer: b. Streaming deploymentultralytics+1
When deploying ML models, what does “model rollback” help to prevent?
a. Prolonged exposure to faulty models
b. Lower accuracy in validation set
c. Overfitting issues
d. Cloud billing errors
Answer: a. Prolonged exposure to faulty modelsaws.amazon+1
What process ensures data consistency between training and deployment phases?
a. Applying identical preprocessing pipelines
b. Increasing hardware resources
c. Frequent data normalization
d. Repeated hyperparameter search
Answer: a. Applying identical preprocessing pipelinesgrowexx+1
Which monitoring metric indicates model degradation after deployment?
a. Increased prediction errors over time
b. Higher CPU allocation
c. Reduced model size
d. Disability of container logs
Answer: a. Increased prediction errors over timeultralytics+1
What’s the primary objective of deploying ML models in production?
a. Providing automated, reliable decision support for real applications
b. Maximizing training set accuracy
c. Reducing RAM usage
d. Maintaining hidden layer activation
Answer: a. Providing automated, reliable decision support for real applicationsgrowexx+1

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