AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO are best practices for model monitoring in production on AWS?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that retraining on a fixed schedule (e.g., daily) is a best practice, when in reality it should be event-driven based on drift or performance metrics.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Monitor input data drift
Option C (Monitor input data drift) is correct because production models degrade when the statistical distribution of incoming features diverges from training data, so tracking data drift with tools like SageMaker Model Monitor detects this covariate shift early. Option E (Monitor prediction drift) is correct because shifts in the distribution of model outputs (e.g., changing class proportions or score distributions) signal concept drift or upstream data issues that require investigation or retraining. Together, input and prediction drift monitoring are the two core best practices for maintaining model quality in production. Option A is wrong because disabling logging removes the observability needed to detect drift, errors, and bias, and logging overhead is typically negligible relative to inference. Option B is wrong because instance type (CPU vs. GPU) is a performance/cost choice, not a monitoring best practice. Option D is wrong because retraining daily is not a best practice by itself; retraining should be triggered by monitored drift or performance degradation, and daily retraining can be costly and destabilizing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable logging to reduce latency
Why it's wrong here
Disabling logging removes the prediction and feature records that drift detection, data capture and CloudWatch metrics depend on, so degradation becomes invisible. Logging is deliberately reduced only where latency budgets are tight and monitoring is handled out-of-band, which contradicts the monitoring requirement here.
- ✗
Use only CPU instances
Why it's wrong here
Restricting inference to CPU instances removes GPU capacity needed for throughput and says nothing about capturing model quality signals. CPU-only fleets suit small models or cost-sensitive batch scoring, not production monitoring, which requires metrics, logging and drift detection regardless of instance type.
- ✓
Monitor input data drift
Why this is correct
Input data drift monitoring detects shifts between production inference data and the training distribution, such as changing customer demographics or feature ranges. This satisfies the best-practice requirement by triggering alerts or retraining before degraded inputs silently corrupt prediction quality in the deployed AWS model.
- ✗
Retrain model daily
Why it's wrong here
Retraining daily is costly and can propagate bad data or unstable weights; monitoring should trigger retraining when drift or performance thresholds are breached. Scheduled frequent retraining suits rapidly shifting domains with cheap pipelines, not a general monitoring best practice.
- ✓
Monitor prediction drift
Why this is correct
Prediction drift monitoring tracks changes in the distribution of model outputs over time, independent of input shifts. It satisfies the production best-practice requirement by revealing degraded model relevance, prompting investigation or retraining when live predictions diverge from expected behaviour.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.