AI MLOps Basics MCQ Questions with Answers (Latest 2026)

Q1. Which option best describes MLOps?

A. A loss function. B. A model architecture family. C. A pure DevOps process for web apps. D. Practices for deploying, monitoring, and updating ML models in production.

Select an answer to check.

Answer: Practices for deploying, monitoring, and updating ML models in production.

Here, Practices for deploying, monitoring, and updating ML models in production. is the right choice. Bridges ML and operations. It aligns directly with what the question asks about which option best describes mlops. A quick elimination of partially true options helps confirm it.

Q2. What is the primary purpose of MLOps?

A. A pure DevOps process for web apps. B. A model architecture family. C. Practices for deploying, monitoring, and updating ML models in production. D. A loss function.

Select an answer to check.

Answer: Practices for deploying, monitoring, and updating ML models in production.

In this case, Practices for deploying, monitoring, and updating ML models in production. is correct. Bridges ML and operations. It aligns directly with what the question asks about what is the primary purpose of mlops. A quick elimination of partially true options helps confirm it.

Q3. Which statement about MLOps is most accurate?

A. A pure DevOps process for web apps. B. Practices for deploying, monitoring, and updating ML models in production. C. A model architecture family. D. A loss function.

Select an answer to check.

Answer: Practices for deploying, monitoring, and updating ML models in production.

The best option here is Practices for deploying, monitoring, and updating ML models in production.. Bridges ML and operations. It aligns directly with what the question asks about which statement about mlops is most accurate. A quick elimination of partially true options helps confirm it.

Q4. How is MLOps best characterized?

A. A model architecture family. B. A pure DevOps process for web apps. C. Practices for deploying, monitoring, and updating ML models in production. D. A loss function.

Select an answer to check.

Answer: Practices for deploying, monitoring, and updating ML models in production.

For this question, Practices for deploying, monitoring, and updating ML models in production. is correct. Bridges ML and operations. It aligns directly with what the question asks about how is mlops best characterized. A quick elimination of partially true options helps confirm it.

Q5. Which option best describes a model registry?

A. Versioned store of trained models with metadata. B. A feature store. C. A monitoring dashboard. D. A vector store of embeddings.

Select an answer to check.

Answer: Versioned store of trained models with metadata.

Versioned store of trained models with metadata. is the correct answer here. Tracks lineage and stage. It aligns directly with what the question asks about which option best describes a model registry. A quick elimination of partially true options helps confirm it.

Q6. What is the primary purpose of a model registry?

A. Versioned store of trained models with metadata. B. A monitoring dashboard. C. A feature store. D. A vector store of embeddings.

Select an answer to check.

Answer: Versioned store of trained models with metadata.

Here, Versioned store of trained models with metadata. is the right choice. Tracks lineage and stage. This matches the core idea being tested around what is the primary purpose of a model. A quick elimination of partially true options helps confirm it.

Q7. Which statement about a model registry is most accurate?

A. A monitoring dashboard. B. Versioned store of trained models with metadata. C. A feature store. D. A vector store of embeddings.

Select an answer to check.

Answer: Versioned store of trained models with metadata.

In this case, Versioned store of trained models with metadata. is correct. Tracks lineage and stage. This matches the core idea being tested around which statement about a model registry is most. A quick elimination of partially true options helps confirm it.

Q8. How is a model registry best characterized?

A. A vector store of embeddings. B. Versioned store of trained models with metadata. C. A monitoring dashboard. D. A feature store.

Select an answer to check.

Answer: Versioned store of trained models with metadata.

The best option here is Versioned store of trained models with metadata.. Tracks lineage and stage. This matches the core idea being tested around how is a model registry best characterized. A quick elimination of partially true options helps confirm it.

Q9. Which option best describes a feature store?

A. A logging system. B. A model registry. C. Service serving consistent features for training and inference. D. A retraining scheduler.

Select an answer to check.

Answer: Service serving consistent features for training and inference.

For this question, Service serving consistent features for training and inference. is correct. Reduces train/serve skew. This matches the core idea being tested around which option best describes a feature store. A quick elimination of partially true options helps confirm it.

Q10. What is the primary purpose of a feature store?

A. Service serving consistent features for training and inference. B. A retraining scheduler. C. A model registry. D. A logging system.

Select an answer to check.

Answer: Service serving consistent features for training and inference.

Service serving consistent features for training and inference. is the correct answer here. Reduces train/serve skew. This matches the core idea being tested around what is the primary purpose of a feature. A quick elimination of partially true options helps confirm it.

Q11. Which statement about a feature store is most accurate?

A. A model registry. B. Service serving consistent features for training and inference. C. A retraining scheduler. D. A logging system.

Select an answer to check.

Answer: Service serving consistent features for training and inference.

Here, Service serving consistent features for training and inference. is the right choice. Reduces train/serve skew. That is exactly the concept behind which statement about a feature store is most in this context. A quick elimination of partially true options helps confirm it.

Q12. How is a feature store best characterized?

A. Service serving consistent features for training and inference. B. A model registry. C. A logging system. D. A retraining scheduler.

Select an answer to check.

Answer: Service serving consistent features for training and inference.

In this case, Service serving consistent features for training and inference. is correct. Reduces train/serve skew. That is exactly the concept behind how is a feature store best characterized in this context. A quick elimination of partially true options helps confirm it.

Q13. Which option best describes CI/CD for ML?

A. Automation for testing/deploying ML pipelines. B. Pretraining recipe. C. A vector index. D. Manual deploys only.

Select an answer to check.

Answer: Automation for testing/deploying ML pipelines.

The best option here is Automation for testing/deploying ML pipelines.. Adds tests, gates, and rollouts. That is exactly the concept behind which option best describes ci/cd for ml in this context. A quick elimination of partially true options helps confirm it.

Q14. What is the primary purpose of CI/CD for ML?

A. A vector index. B. Automation for testing/deploying ML pipelines. C. Pretraining recipe. D. Manual deploys only.

Select an answer to check.

Answer: Automation for testing/deploying ML pipelines.

For this question, Automation for testing/deploying ML pipelines. is correct. Adds tests, gates, and rollouts. That is exactly the concept behind what is the primary purpose of ci/cd for in this context. A quick elimination of partially true options helps confirm it.

Q15. Which statement about CI/CD for ML is most accurate?

A. Pretraining recipe. B. Automation for testing/deploying ML pipelines. C. Manual deploys only. D. A vector index.

Select an answer to check.

Answer: Automation for testing/deploying ML pipelines.

Automation for testing/deploying ML pipelines. is the correct answer here. Adds tests, gates, and rollouts. That is exactly the concept behind which statement about ci/cd for ml is most in this context. A quick elimination of partially true options helps confirm it.

Q16. How is CI/CD for ML best characterized?

A. A vector index. B. Manual deploys only. C. Automation for testing/deploying ML pipelines. D. Pretraining recipe.

Select an answer to check.

Answer: Automation for testing/deploying ML pipelines.

Here, Automation for testing/deploying ML pipelines. is the right choice. Adds tests, gates, and rollouts. It fits the requirement in the prompt about how is ci/cd for ml best characterized. A quick elimination of partially true options helps confirm it.

Q17. Which option best describes offline training?

A. No retraining ever. B. Manual one-time training only. C. Periodic batch training of models. D. Online streaming updates only.

Select an answer to check.

Answer: Periodic batch training of models.

In this case, Periodic batch training of models. is correct. Standard pattern for many models. It fits the requirement in the prompt about which option best describes offline training. A quick elimination of partially true options helps confirm it.

Q18. What is the primary purpose of offline training?

A. Manual one-time training only. B. No retraining ever. C. Online streaming updates only. D. Periodic batch training of models.

Select an answer to check.

Answer: Periodic batch training of models.

The best option here is Periodic batch training of models.. Standard pattern for many models. It fits the requirement in the prompt about what is the primary purpose of offline training. A quick elimination of partially true options helps confirm it.

Q19. Which statement about offline training is most accurate?

A. Manual one-time training only. B. Periodic batch training of models. C. No retraining ever. D. Online streaming updates only.

Select an answer to check.

Answer: Periodic batch training of models.

For this question, Periodic batch training of models. is correct. Standard pattern for many models. It fits the requirement in the prompt about which statement about offline training is most accurate. A quick elimination of partially true options helps confirm it.

Q20. How is offline training best characterized?

A. Online streaming updates only. B. Periodic batch training of models. C. Manual one-time training only. D. No retraining ever.

Select an answer to check.

Answer: Periodic batch training of models.

Periodic batch training of models. is the correct answer here. Standard pattern for many models. It fits the requirement in the prompt about how is offline training best characterized. A quick elimination of partially true options helps confirm it.

Q21. Which option best describes online learning?

A. Random sampling only. B. Pure offline batch training. C. Continuously updating model from streaming data. D. No retraining at all.

Select an answer to check.

Answer: Continuously updating model from streaming data.

Here, Continuously updating model from streaming data. is the right choice. Faster reaction to drift. This is the most accurate statement for which option best describes online learning. A quick elimination of partially true options helps confirm it.

Q22. What is the primary purpose of online learning?

A. Pure offline batch training. B. No retraining at all. C. Random sampling only. D. Continuously updating model from streaming data.

Select an answer to check.

Answer: Continuously updating model from streaming data.

In this case, Continuously updating model from streaming data. is correct. Faster reaction to drift. This is the most accurate statement for what is the primary purpose of online learning. A quick elimination of partially true options helps confirm it.

Q23. Which statement about online learning is most accurate?

A. Pure offline batch training. B. Continuously updating model from streaming data. C. No retraining at all. D. Random sampling only.

Select an answer to check.

Answer: Continuously updating model from streaming data.

The best option here is Continuously updating model from streaming data.. Faster reaction to drift. This is the most accurate statement for which statement about online learning is most accurate. A quick elimination of partially true options helps confirm it.

Q24. How is online learning best characterized?

A. Pure offline batch training. B. Continuously updating model from streaming data. C. No retraining at all. D. Random sampling only.

Select an answer to check.

Answer: Continuously updating model from streaming data.

For this question, Continuously updating model from streaming data. is correct. Faster reaction to drift. This is the most accurate statement for how is online learning best characterized. A quick elimination of partially true options helps confirm it.

Q25. Which option best describes model drift?

A. Degradation due to changing data/world. B. High variance features. C. Outlier presence. D. Class imbalance.

Select an answer to check.

Answer: Degradation due to changing data/world.

Degradation due to changing data/world. is the correct answer here. Requires monitoring + retraining. This is the most accurate statement for which option best describes model drift. A quick elimination of partially true options helps confirm it.

Q26. What is the primary purpose of model drift?

A. High variance features. B. Class imbalance. C. Degradation due to changing data/world. D. Outlier presence.

Select an answer to check.

Answer: Degradation due to changing data/world.

Here, Degradation due to changing data/world. is the right choice. Requires monitoring + retraining. It aligns directly with what the question asks about what is the primary purpose of model drift. The other options are either incomplete or contextually incorrect.

Q27. Which statement about model drift is most accurate?

A. Degradation due to changing data/world. B. High variance features. C. Outlier presence. D. Class imbalance.

Select an answer to check.

Answer: Degradation due to changing data/world.

In this case, Degradation due to changing data/world. is correct. Requires monitoring + retraining. It aligns directly with what the question asks about which statement about model drift is most accurate. The other options are either incomplete or contextually incorrect.

Q28. How is model drift best characterized?

A. Outlier presence. B. Class imbalance. C. Degradation due to changing data/world. D. High variance features.

Select an answer to check.

Answer: Degradation due to changing data/world.

The best option here is Degradation due to changing data/world.. Requires monitoring + retraining. It aligns directly with what the question asks about how is model drift best characterized. The other options are either incomplete or contextually incorrect.

Q29. Which option best describes data drift?

A. Change in input distributions. B. Label shift. C. Concept drift in mapping. D. High variance features.

Select an answer to check.

Answer: Change in input distributions.

For this question, Change in input distributions. is correct. Monitor and alert. It aligns directly with what the question asks about which option best describes data drift. The other options are either incomplete or contextually incorrect.

Q30. What is the primary purpose of data drift?

A. Concept drift in mapping. B. Change in input distributions. C. High variance features. D. Label shift.

Select an answer to check.

Answer: Change in input distributions.

Change in input distributions. is the correct answer here. Monitor and alert. It aligns directly with what the question asks about what is the primary purpose of data drift. The other options are either incomplete or contextually incorrect.

Q31. Which statement about data drift is most accurate?

A. Change in input distributions. B. Concept drift in mapping. C. High variance features. D. Label shift.

Select an answer to check.

Answer: Change in input distributions.

Here, Change in input distributions. is the right choice. Monitor and alert. This matches the core idea being tested around which statement about data drift is most accurate. The other options are either incomplete or contextually incorrect.

Q32. How is data drift best characterized?

A. Concept drift in mapping. B. Label shift. C. Change in input distributions. D. High variance features.

Select an answer to check.

Answer: Change in input distributions.

In this case, Change in input distributions. is correct. Monitor and alert. This matches the core idea being tested around how is data drift best characterized. The other options are either incomplete or contextually incorrect.

Q33. Which option best describes concept drift?

A. Data drift in inputs only. B. Label noise. C. Change in input→output relationship. D. Class imbalance.

Select an answer to check.

Answer: Change in input→output relationship.

The best option here is Change in input→output relationship.. Hard to detect; needs labels. This matches the core idea being tested around which option best describes concept drift. The other options are either incomplete or contextually incorrect.

Q34. What is the primary purpose of concept drift?

A. Data drift in inputs only. B. Label noise. C. Change in input→output relationship. D. Class imbalance.

Select an answer to check.

Answer: Change in input→output relationship.

For this question, Change in input→output relationship. is correct. Hard to detect; needs labels. This matches the core idea being tested around what is the primary purpose of concept drift. The other options are either incomplete or contextually incorrect.

Q35. Which statement about concept drift is most accurate?

A. Label noise. B. Class imbalance. C. Data drift in inputs only. D. Change in input→output relationship.

Select an answer to check.

Answer: Change in input→output relationship.

Change in input→output relationship. is the correct answer here. Hard to detect; needs labels. This matches the core idea being tested around which statement about concept drift is most accurate. The other options are either incomplete or contextually incorrect.

Q36. How is concept drift best characterized?

A. Class imbalance. B. Change in input→output relationship. C. Data drift in inputs only. D. Label noise.

Select an answer to check.

Answer: Change in input→output relationship.

Here, Change in input→output relationship. is the right choice. Hard to detect; needs labels. That is exactly the concept behind how is concept drift best characterized in this context. The other options are either incomplete or contextually incorrect.

Q37. Which option best describes a feature pipeline?

A. A vector index. B. A logging library. C. A retry middleware. D. ETL for features used in training/serving.

Select an answer to check.

Answer: ETL for features used in training/serving.

In this case, ETL for features used in training/serving. is correct. Often shared across teams. That is exactly the concept behind which option best describes a feature pipeline in this context. The other options are either incomplete or contextually incorrect.

Q38. What is the primary purpose of a feature pipeline?

A. ETL for features used in training/serving. B. A retry middleware. C. A logging library. D. A vector index.

Select an answer to check.

Answer: ETL for features used in training/serving.

The best option here is ETL for features used in training/serving.. Often shared across teams. That is exactly the concept behind what is the primary purpose of a feature in this context. The other options are either incomplete or contextually incorrect.

Q39. Which statement about a feature pipeline is most accurate?

A. A logging library. B. A retry middleware. C. A vector index. D. ETL for features used in training/serving.

Select an answer to check.

Answer: ETL for features used in training/serving.

For this question, ETL for features used in training/serving. is correct. Often shared across teams. That is exactly the concept behind which statement about a feature pipeline is most in this context. The other options are either incomplete or contextually incorrect.

Q40. How is a feature pipeline best characterized?

A. A logging library. B. ETL for features used in training/serving. C. A retry middleware. D. A vector index.

Select an answer to check.

Answer: ETL for features used in training/serving.

ETL for features used in training/serving. is the correct answer here. Often shared across teams. That is exactly the concept behind how is a feature pipeline best characterized in this context. The other options are either incomplete or contextually incorrect.

Q41. Which option best describes model monitoring?

A. Static evaluation only. B. Tracking metrics, drift, and quality in production. C. Logging only. D. Pretraining only.

Select an answer to check.

Answer: Tracking metrics, drift, and quality in production.

Here, Tracking metrics, drift, and quality in production. is the right choice. Enables timely interventions. It fits the requirement in the prompt about which option best describes model monitoring. The other options are either incomplete or contextually incorrect.

Q42. What is the primary purpose of model monitoring?

A. Tracking metrics, drift, and quality in production. B. Static evaluation only. C. Logging only. D. Pretraining only.

Select an answer to check.

Answer: Tracking metrics, drift, and quality in production.

In this case, Tracking metrics, drift, and quality in production. is correct. Enables timely interventions. It fits the requirement in the prompt about what is the primary purpose of model monitoring. The other options are either incomplete or contextually incorrect.

Q43. Which statement about model monitoring is most accurate?

A. Static evaluation only. B. Pretraining only. C. Logging only. D. Tracking metrics, drift, and quality in production.

Select an answer to check.

Answer: Tracking metrics, drift, and quality in production.

The best option here is Tracking metrics, drift, and quality in production.. Enables timely interventions. It fits the requirement in the prompt about which statement about model monitoring is most accurate. The other options are either incomplete or contextually incorrect.

Q44. How is model monitoring best characterized?

A. Tracking metrics, drift, and quality in production. B. Pretraining only. C. Logging only. D. Static evaluation only.

Select an answer to check.

Answer: Tracking metrics, drift, and quality in production.

For this question, Tracking metrics, drift, and quality in production. is correct. Enables timely interventions. It fits the requirement in the prompt about how is model monitoring best characterized. The other options are either incomplete or contextually incorrect.

Q45. Which option best describes inference latency?

A. DNS lookup time. B. Training time only. C. Disk read time only. D. Time per prediction served.

Select an answer to check.

Answer: Time per prediction served.

Time per prediction served. is the correct answer here. User-facing SLO component. It fits the requirement in the prompt about which option best describes inference latency. The other options are either incomplete or contextually incorrect.

Q46. What is the primary purpose of inference latency?

A. Training time only. B. Disk read time only. C. Time per prediction served. D. DNS lookup time.

Select an answer to check.

Answer: Time per prediction served.

Here, Time per prediction served. is the right choice. User-facing SLO component. This is the most accurate statement for what is the primary purpose of inference latency. The other options are either incomplete or contextually incorrect.

Q47. Which statement about inference latency is most accurate?

A. Training time only. B. Disk read time only. C. DNS lookup time. D. Time per prediction served.

Select an answer to check.

Answer: Time per prediction served.

In this case, Time per prediction served. is correct. User-facing SLO component. This is the most accurate statement for which statement about inference latency is most accurate. The other options are either incomplete or contextually incorrect.

Q48. How is inference latency best characterized?

A. Time per prediction served. B. Disk read time only. C. Training time only. D. DNS lookup time.

Select an answer to check.

Answer: Time per prediction served.

The best option here is Time per prediction served.. User-facing SLO component. This is the most accurate statement for how is inference latency best characterized. The other options are either incomplete or contextually incorrect.

Q49. Which option best describes throughput?

A. CPU cycles. B. Predictions per second a system can serve. C. Bytes per second. D. Training samples per second only.

Select an answer to check.

Answer: Predictions per second a system can serve.

For this question, Predictions per second a system can serve. is correct. Capacity metric. This is the most accurate statement for which option best describes throughput. The other options are either incomplete or contextually incorrect.

Q50. What is the primary purpose of throughput?

A. Bytes per second. B. CPU cycles. C. Training samples per second only. D. Predictions per second a system can serve.

Select an answer to check.

Answer: Predictions per second a system can serve.

Predictions per second a system can serve. is the correct answer here. Capacity metric. This is the most accurate statement for what is the primary purpose of throughput. The other options are either incomplete or contextually incorrect.