MCQs

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Overall MCQs for Reference

1. What is the primary purpose of model deployment in machine learning?
   a. To visualize data
   b. To make models available for production use
   c. To train models
   d. To clean the dataset
   Answer: b. To make models available for production usegeeksforgeeks​
2. Which method is commonly used for real-time model deployment?
   a. Batch file processing
   b. REST API
   c. CSV export
   d. FTP server
   Answer: b. REST APIstudocu​
3. What is the key consideration for deploying models on edge devices?
   a. High computation resources
   b. Network latency
   c. On-device inference capability
   d. Unlimited storage
   Answer: c. On-device inference capabilitygrowexx​
4. Which platform offers scalable cloud-based model deployment?
   a. MS Excel
   b. TensorFlow Serving
   c. Notepad++
   d. MS Word
   Answer: b. TensorFlow Servinggeeksforgeeks​
5. What is the main advantage of containerization (e.g., Docker) in model deployment?
   a. Increases latency
   b. Provides consistent environments
   c. Supports only Windows
   d. Reduces model accuracy
   Answer: b. Provides consistent environmentsultralytics​
6. Which phase is critical before model deployment to verify model performance?
   a. Data labeling
   b. Testing stage
   c. Visualization
   d. Model naming
   Answer: b. Testing stageexploredatabase​
7. What is model versioning?
   a. Training multiple models
   b. Keeping track of model updates
   c. Deleting old models
   d. Ignoring data changes
   Answer: b. Keeping track of model updatesgrowexx​
8. In batch deployment, how are predictions generated?
   a. In real-time as data comes in
   b. On large sets of data periodically
   c. Only once
   d. Never
   Answer: b. On large sets of data periodicallystudocu​
9. Which deployment strategy minimizes latency for predictions?
   a. Batch deployment
   b. Online (real-time) deployment
   c. Manual calculations
   d. Email reports
   Answer: b. Online (real-time) deploymentstudocu​
10. What is a shadow deployment in MLOps?
    a. Deploying without testing
    b. Deploying a new model alongside an existing one for comparison
    c. Retraining the same model
    d. Ignoring current models
    Answer: b. Deploying a new model alongside an existing one for comparisonscmgalaxy​
11. Why is security important in model deployment?
    a. To improve prediction speed
    b. To ensure only authorized access and secure data transmission
    c. To increase CPU usage
    d. To reduce storage costs
    Answer: b. To ensure only authorized access and secure data transmissionultralytics​
12. What is model drift?
    a. Model improving after deployment
    b. Model performance degrading over time
    c. Model accuracy always being high
    d. Model retraining automatically
    Answer: b. Model performance degrading over timeqwak​
13. Which cloud service is widely used for ML model deployment?
    a. AWS SageMaker
    b. Yahoo Mail
    c. Reddit
    d. Instagram
    Answer: a. AWS SageMakerkoenig-solutions​
14. Why is CI/CD useful in ML model deployment?
    a. Automates the deployment process
    b. Slows updates
    c. Requires manual changes
    d. Only works offline
    Answer: a. Automates the deployment processmirantis​
15. What helps make ML models accessible for users after deployment?
    a. Validation sets
    b. APIs and web services
    c. Only local devices
    d. Binary files
    Answer: b. APIs and web servicesplacementpreparation​
16. Which deployment environment is best for IoT and low-latency applications?
    a. Cloud
    b. Edge computing
    c. On-premises data center
    d. Mainframe
    Answer: b. Edge computinggrowexx​
17. What is the purpose of testing with unseen data before deployment?
    a. To verify generalization ability
    b. To confirm training accuracy only
    c. To ensure overfitting
    d. To adjust hyperparameters
    Answer: a. To verify generalization abilityexploredatabase​
18. What can be implemented to ensure auditability in deployments?
    a. Ignoring logs
    b. Audit trails and logging
    c. Random deletion
    d. Data mixing
    Answer: b. Audit trails and loggingmirantis​
19. What is model rollback?
    a. Going back to a previously deployed version after a failure
    b. Deleting logs
    c. Ignoring errors
    d. Increasing model size
    Answer: a. Going back to a previously deployed version after a failuremirantis​
20. What is the primary concern when deploying models in regulated industries?
    a. User interface
    b. Compliance and data privacy
    c. Model complexity
    d. Storage space
    Answer: b. Compliance and data privacyultralytics​
21. Which format is commonly used for exchanging models between frameworks?
    a. ONNX
    b. JPEG
    c. TXT
    d. SVG
    Answer: a. ONNXultralytics​
22. What is A/B testing in model deployment?
    a. Random sampling technique
    b. Comparing two versions of models with live user data
    c. Ignoring previous version
    d. Manual evaluation
    Answer: b. Comparing two versions of models with live user dataqwak​
23. What is hot swapping in model deployment?
    a. Changing model weights in memory without restarting services
    b. Deleting current models
    c. Only works with static files
    d. Requires reinstallation
    Answer: a. Changing model weights in memory without restarting servicesgrowexx​
24. What ensures performance consistency in different deployment environments?
    a. Containerization (Docker)
    b. Manual installations
    c. Browser plugins
    d. Unmanaged code
    Answer: a. Containerization (Docker)ultralytics​
25. When should you retrain a deployed ML model?
    a. When data distribution changes
    b. At random times
    c. Never
    d. Only after errors
    Answer: a. When data distribution changesqwak​
26. What is blue-green deployment?
    a. Switching between two production environments to minimize downtime
    b. Upgrading hardware
    c. Manual retraining
    d. Only for visualization
    Answer: a. Switching between two production environments to minimize downtimeqwak​
27. Which protocol is essential for secure API communication during model deployment?
    a. TLS
    b. FTP
    c. HTTP only
    d. SSH only
    Answer: a. TLSultralytics​
28. What is the role of model monitoring post deployment?
    a. Detecting drift and ensuring model performance
    b. Ignoring prediction errors
    c. Increasing training speed
    d. Decreasing storage
    Answer: a. Detecting drift and ensuring model performanceqwak​
29. Which tool can deploy ML models using Python APIs and web servers?
    a. Flask
    b. LibreOffice
    c. Acrobat Reader
    d. Nano
    Answer: a. Flasksattacademy​
30. What does scalability mean in model deployment?
    a. Ability to handle increased workload efficiently
    b. Ignoring extra users
    c. Limiting prediction requests
    d. Only running on personal laptops
    Answer: a. Ability to handle increased workload efficientlygrowexx​
31. What is the effect of poor resource allocation during deployment?
    a. Slowed inference and possible model failures
    b. Increased accuracy
    c. Enhanced visuals
    d. Unlimited storage
    Answer: a. Slowed inference and possible model failuresgrowexx​
32. What is the goal of canary deployment?
    a. Rolling out new changes to a small subset of users first
    b. Deploying to everyone without testing
    c. Only works for mobile apps
    d. Removing testing stages
    Answer: a. Rolling out new changes to a small subset of users firstqwak​
33. Which component is crucial for batch ML model deployment?
    a. Scheduled job orchestrator (e.g., Airflow)
    b. Real-time dashboard
    c. Static webpage
    d. File manager
    Answer: a. Scheduled job orchestrator (e.g., Airflow)studocu​
34. What is the main reason for quantization in deployed models?
    a. Improve model efficiency on resource-constrained devices
    b. Increase memory usage
    c. Lower accuracy
    d. Add user data
    Answer: a. Improve model efficiency on resource-constrained devicesultralytics​
35. Which deployment strategy is recommended for large financial transactions?
    a. Batch deployment
    b. Online deployment only
    c. Hot swapping
    d. Shadow deployment
    Answer: a. Batch deploymentstudocu​
36. What is the importance of using feature stores in ML deployment?
    a. Ensures consistent feature values for training and inference
    b. Decreases memory
    c. Unused in practice
    d. Only works for images
    Answer: a. Ensures consistent feature values for training and inferenceultralytics​
37. What helps avoid data leakage during model deployment?
    a. Strict separation of training and production data pipelines
    b. Storing all data together
    c. Mixing user sessions
    d. Ignoring pipeline design
    Answer: a. Strict separation of training and production data pipelinesultralytics​
38. What technical approach is recommended for automating deployment and scaling in cloud ML environments?
    a. Kubernetes orchestration
    b. Manual service start
    c. Spreadsheet macros
    d. Browser add-ons
    Answer: a. Kubernetes orchestrationqwak​
39. Why use model pruning before deployment to mobile?
    a. Reduces model size and accelerates inference
    b. Adds complexity
    c. Decreases throughput
    d. Removes security
    Answer: a. Reduces model size and accelerates inferenceultralytics​
40. What is the benefit of knowledge distillation for deployment?
    a. Enables smaller models with near-original accuracy
    b. Makes models harder to train
    c. Reduces interpretability
    d. Increases drift
    Answer: a. Enables smaller models with near-original accuracyultralytics​
41. What is the main risk in exposing ML prediction APIs publicly?
    a. Security vulnerabilities and unauthorized access
    b. Increasing visibility
    c. Automating error reporting
    d. Improving accuracy
    Answer: a. Security vulnerabilities and unauthorized accessultralytics​
42. Which cloud deployment mode provides the lowest latency?
    a. Edge computing
    b. Shared hosting
    c. Batch deployment
    d. Private cloud offline
    Answer: a. Edge computinggrowexx​
43. Which monitoring metric is most critical post deployment?
    a. Prediction accuracy
    b. Visualization color schemes
    c. User interface design
    d. Datasets naming
    Answer: a. Prediction accuracyqwak​
44. What enables quick restoration of service after failed deployment?
    a. Model rollback
    b. Model retraining
    c. Model versioning
    d. Model expansion
    Answer: a. Model rollbackmirantis​
45. Which step comes first in the ML model deployment workflow?
    a. Model development in training environment
    b. Cloud hosting
    c. User sign-in
    d. Email notification
    Answer: a. Model development in training environmentgeeksforgeeks​
46. When should edge deployment be prioritized?
    a. For time-sensitive and privacy-centric applications
    b. For static archive storage only
    c. When using large clusters
    d. For visualization tasks
    Answer: a. For time-sensitive and privacy-centric applicationsgrowexx​
47. Why synchronize model and preprocessing code in deployment?
    a. Ensures consistency between training and inference
    b. Decreases accuracy
    c. Increases latency
    d. Adds user inputs
    Answer: a. Ensures consistency between training and inferenceultralytics​
48. Which deployment tool supports scalable inference and traffic routing?
    a. TensorFlow Serving
    b. Acrobat Reader
    c. Office Suite
    d. Image Viewer
    Answer: a. TensorFlow Servinggeeksforgeeks​
49. What does API throttling help prevent in ML model deployment?
    a. Overload and ensure fair resource utilization
    b. Increase connection speed
    c. Slow down accuracy
    d. Delete user queries
    Answer: a. Overload and ensure fair resource utilizationultralytics​
50. Why is regular model monitoring and feedback collection essential post deployment?
    a. Maintains performance and reliability over time
    b. Decreases model usability
    c. Increases code complexity
    d. Ignores drift
    Answer: a. Maintains performance and reliability over timeqwak​

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