Knowledge check

Knowledge check

Complete

What is the primary benefit of implementing an automated rollback mechanism during ML deployment?

A) It ensures models automatically retrain when performance drops.B) It allows the deployment team to delay monitoring until postlaunch.C) It enables fast recovery by reverting to a previously validated model version when issues are detected.D) It permanently disables the faulty model from future deployment.

Which deployment strategy is most suitable when you want to test a new ML model in production without affecting user-facing outcomes?

A) Blue/green deploymentB) Canary deploymentC) Dark launchD) A/B testing

What is the primary purpose of implementing monitoring for data and model drift in production?

A) To retrain the model automatically on a fixed scheduleB) To compare user engagement across different modelsC) To detect changes in data patterns or model performance over timeD) To track GPU usage during inference

In which scenario is a blue/green deployment strategy most appropriate?

A) When you need to gradually test a new model's predictions on a small user groupB) When you want to compare two models' outputs in real time for performance differencesC) When you want to switch between identical environments to enable fast rollbackD) When you want to collect hidden metrics from a shadow model

A model deployed via a real-time API is experiencing latency spikes under high load. What is the most appropriate first mitigation strategy?

A) Add more data to the training set.B) The API model fails under high concurrency.C) Users misunderstand a model's decision explanation.D) Legal policies change regarding data retention.

Which of the following best characterises a technical risk in ML deployment?

A) A model predicts differently across geographic markets.B) The API model fails under high concurrency.C) Users misunderstand a model's decision explanation.D) Legal policies change regarding data retention.

What is the key benefit of including a human-in-the-loop (HITL) process in high-stakes ML deployment?

A) It eliminates all technical risks.B) It provides human review for edge cases and sensitive decisions.C) It guarantees the model will not be biased.D) It simplifies the model's architecture.

Which of the following risks is most associated with automated retraining systems?

A) Increased model fairness over timeB) Feedback loops that reinforce model errorsC) Shorter model deployment cyclesD) Better generalisation to unseen data

Why is it important to version APIs and model outputs during deployment?

A) To reduce model accuracy variationB) To simplify retraining proceduresC) To ensure backward compatibility and prevent downstream system failuresD) To collect metrics across more endpoints

A financial services company is deploying a credit scoring model. What combination of risk mitigation strategies would best address compliance, explainability, and user safety?

A) GPU acceleration, cloud autoscaling, A/B testingB) SHAP values, audit trails, legal reviewC) Feedback loops, batch scoring, edge deploymentD) Caching, shadow models, automated retraining

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