MLA-C01 ML Model Development Practice Question

A machine learning engineer is deploying a model to a SageMaker endpoint for real-time inference. The model must return predictions within 100 milliseconds for 95% of requests. The engineer wants to monitor the endpoint's latency and automatically roll back if latency exceeds the threshold. Which combination of SageMaker features should be used?

⚠ Common exam trap

The trap here is assuming that SageMaker Model Monitor handles latency SLOs, but it focuses on data and model quality drift, not performance metrics like latency.

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

✓

SageMaker deployment guardrails with a blue/green deployment and CloudWatch alarms for latency.

SageMaker deployment guardrails are designed to safely update endpoints with automatic rollback. By associating a CloudWatch alarm that monitors model latency, the guardrail can trigger a rollback if the alarm state changes to ALARM. This provides the required automatic protection. Other features like Model Monitor, Inference Recommender, or Clarify do not offer this latency-based rollback capability out of the box.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
SageMaker Model Monitor with a custom monitoring schedule and AWS Lambda for rollback.
Why it's wrong here
SageMaker Model Monitor is designed to detect data drift and model quality issues, not to enforce latency SLOs. While it can monitor some metrics, it does not natively trigger automatic rollbacks based on latency thresholds. Using a custom Lambda adds complexity and is not the intended use. This combination does not directly provide the required latency-based rollback.
✓
SageMaker deployment guardrails with a blue/green deployment and CloudWatch alarms for latency.
Why this is correct
SageMaker deployment guardrails support blue/green and linear deployments with automatic rollback triggered by CloudWatch alarms. You can create a CloudWatch alarm on the endpoint's model latency metric, and configure the guardrail to roll back if the alarm fires. This directly satisfies the need to monitor latency and automatically roll back when the threshold is breached.
✗
SageMaker Clarify for bias detection and AWS Step Functions for rollback orchestration.
Why it's wrong here
SageMaker Clarify is for bias detection and explainability, not latency monitoring. Step Functions can orchestrate rollback but would need a trigger from a monitoring system. This combination lacks a direct integration to monitor endpoint latency and trigger rollback, making it overly complex and not purpose-built for the scenario.
✗
SageMaker Inference Recommender to select the optimal instance type and automatic scaling.
Why it's wrong here
Inference Recommender helps choose instance types and configurations for cost and performance, but it does not provide runtime monitoring or automatic rollback. Automatic scaling adjusts capacity based on load but does not roll back deployments. Neither feature enforces latency SLOs with rollback, so this combination does not meet the requirement.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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