NSCT – Model Deployment & MLOps Basics MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 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 onlyShow All Answers 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
