Amazon Exam AIF-C01 Topic 1 Question 30 Discussion

Actual exam question for Amazon's AIF-C01 exam

Question #: 30
Topic #: 1

A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time. Which AWS service or feature meets these requirements?

A. Amazon Rekognition B. Amazon SageMaker Clarify C. Amazon Comprehend D. Amazon SageMaker Model Monitor

AAmazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.

BAmazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions. While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.

CAmazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.

DAmazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, ''Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation.'' (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.

Show Suggested Answer

Suggested Answer: D

The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:

AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html)

AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)

by Trevor at Nov 01, 2025, 08:11 AM

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Ira

2 hours ago

I'm a bit confused by the differences between the services mentioned. Do Rekognition, Clarify, and Comprehend not have any model monitoring capabilities at all? I want to make sure I fully understand the distinctions before committing to an answer.

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Laticia

6 days ago

I think the key here is the requirement to monitor model quality drift over time. That's a very specific need, and Amazon SageMaker Model Monitor seems designed for exactly that purpose. I'd go with D as the best option.

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Margart

11 days ago

Hmm, I'm not sure about this one. The question mentions analyzing viewer behavior and demographics, so I'm wondering if Amazon Comprehend might be a better fit for the text analysis aspect.

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Annita

17 days ago

The answer is clearly D. Amazon SageMaker Model Monitor is the service that meets the requirements of deploying a custom ML model and monitoring for quality drift over time.

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