AIF-C01 Applications of Foundation Models Practice Question
A company uses Amazon Bedrock with a custom model deployed via Amazon SageMaker. They want to monitor for data drift in input prompts over time. Which AWS service is best suited for this?
⚠ Common exam trap
Many candidates confuse general monitoring services like CloudWatch with specialized ML monitoring tools, assuming CloudWatch can handle data drift detection when it actually lacks the statistical analysis required for such tasks.
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
✓
Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor is the correct choice because it is specifically designed to detect data drift in machine learning models, including input prompts for custom models deployed via SageMaker. It continuously monitors the distribution of input data against a baseline and alerts when drift occurs, which aligns with the requirement to monitor input prompts over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon CloudWatch
Why it's wrong here
CloudWatch tracks operational metrics and logs, not statistical distribution shifts in prompt text; drift detection requires comparing baseline and live data distributions, which SageMaker Model Monitor performs. CloudWatch is tempting because it collects custom metrics and can raise alarms, and would suit latency, error-rate or invocation-count monitoring.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor captures incoming request data and compares it against a baseline, detecting drift in prompt feature distributions over time. This satisfies the requirement to monitor input prompts for data drift on a custom SageMaker-deployed model.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail records API activity and management events, not the statistical distribution of prompt inputs, so it cannot detect drift. It would be correct for auditing who invoked the endpoint, whereas drift monitoring requires comparing input distributions over time.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data in S3 but performs no drift computation, baseline comparison or alerting; SageMaker Model Monitor provides that. Athena is tempting because prompt logs can be stored in S3 and queried with SQL, and it would be correct for ad-hoc analysis of historical prompt data.
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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.