1. . Model deployment in machine learning refers to:
(A) Encrypting the model only
(B) Making a trained ML model available for use in real-world applications
(C) Compressing datasets only
(D) Backup only
2. . MLOps stands for:
(A) Compressing ML models
(B) Encrypting ML pipelines
(C) Machine Learning Operations, a practice for deploying, monitoring, and managing ML models in production
(D) Backup only
3. . Continuous Integration (CI) in MLOps involves:
(A) Compressing models
(B) Encrypting code
(C) Automatically testing and integrating changes in code and ML models
(D) Backup only
4. . Continuous Deployment (CD) in MLOps refers to:
(A) Backup only
(B) Encrypting deployment
(C) Compressing deployment
(D) Automatically deploying tested models into production environments
5. . Model versioning is important because:
(A) It tracks changes and allows rollback to previous versions if needed
(B) Encrypting versions
(C) Compressing versions
(D) Backup only
6. . Model serving is:
(A) Encrypting predictions
(B) Making the trained ML model accessible via APIs or web services for predictions
(C) Compressing outputs
(D) Backup only
7. . Monitoring in MLOps helps to:
(A) Track model performance, detect drift, and maintain accuracy in production
(B) Encrypt monitoring data
(C) Compress logs
(D) Backup only
8. . Model drift occurs when:
(A) Backup only
(B) Encrypting drift
(C) Compressing drift
(D) The model's performance degrades over time due to changes in data distribution
9. . A/B testing in ML deployment is used to:
(A) Compress test data
(B) Encrypt test data
(C) Compare performance of two models or variations in production
(D) Backup only
10. . Containerization (e.g., Docker) in ML deployment allows:
(A) Packaging models with dependencies to ensure consistency across environments
(B) Encrypting containers
(C) Compressing containers
(D) Backup only
11. . Orchestration tools like Kubernetes help to:
(A) Compress orchestration
(B) Encrypt orchestration
(C) Automate deployment, scaling, and management of containerized ML models
(D) Backup only
12. . Feature stores in MLOps are used to:
(A) Compress features
(B) Encrypt features
(C) Store, manage, and serve features consistently for training and inference
(D) Backup only
13. . CI/CD pipelines in MLOps help to:
(A) Encrypt pipelines
(B) Automate testing, integration, and deployment of ML models
(C) Compress pipelines
(D) Backup only
14. . Rolling updates in model deployment mean:
(A) Compressing updates
(B) Encrypting updates
(C) Gradually replacing old models with new ones to minimize downtime
(D) Backup only
15. . Canary deployment refers to:
(A) Compressing canary deployment
(B) Encrypting canary models
(C) Deploying a new model to a small subset of users to test performance before full rollout
(D) Backup only
16. . Logging in MLOps is important to:
(A) Backup only
(B) Encrypt logs
(C) Compress logs
(D) Track predictions, errors, and system performance for debugging and auditing
17. . Model reproducibility ensures:
(A) Backup only
(B) Encrypting reproducibility
(C) Compressing models
(D) Same results can be obtained when rerunning experiments with the same data and code
18. . Scalability in ML deployment refers to:
(A) Compressing scalability
(B) Encrypting scalability
(C) Ability to handle increasing data volume and user requests efficiently
(D) Backup only
19. . Cloud platforms like AWS, GCP, and Azure provide:
(A) Compressing cloud ML models
(B) Encrypting cloud ML models
(C) Services for ML model deployment, monitoring, and scaling
(D) Backup only
20. . The main purpose of MLOps and model deployment is to:
(A) Ensure that ML models are reliable, scalable, maintainable, and performant in real-world production environments
(B) Encrypt all ML models
(C) Compress datasets
(D) Backup only