hardMultiple Choice
MLA-C01 Practice Question: A healthcare company is deploying a model for…
A healthcare company is deploying a model for predicting patient outcomes. The model must be deployed across multiple AWS accounts to meet compliance requirements. Each account has its own Amazon SageMaker endpoint. The company wants to centralize monitoring of model performance without exposing data across accounts. Which solution should the company use?
⚠ Common exam trap
It's easy for candidates to confuse data replication (which exposes raw data) with metric aggregation (which exposes only statistical summaries), leading candidates to pick Option B despite its compliance violation.
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
✓
Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability.
It uses SageMaker Model Monitor in each account to detect data drift and model degradation locally, then publishes custom metrics to a central CloudWatch account via cross-account observability. This approach centralizes monitoring without moving raw inference data across accounts, satisfying the compliance requirement of not exposing data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Establish VPC peering between accounts and call the endpoints from a central monitoring service.
Why it's wrong here
VPC peering provides network connectivity, not cross-account monitoring telemetry; it cannot aggregate endpoint metrics or capture data without moving it between accounts, breaching the isolation requirement. Peering suits private connectivity between specific VPCs, such as shared services access, not centralised SageMaker Model Monitor collection.
- ✗
Replicate the inference data to a central S3 bucket in the management account using cross-account replication, then run Model Monitor centrally.
Why it's wrong here
Cross-account replication copies inference data into a central bucket, directly exposing patient data across accounts and violating the stated compliance constraint. Replication suits centralised analytics or backup consolidation where data movement is permitted, not monitoring that must remain within each account boundary.
- ✓
Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability.
Why this is correct
Cross-account observability lets each account's Model Monitor publish metrics into a central CloudWatch account, so performance is aggregated without moving patient data between accounts. This satisfies the compliance constraint requiring per-account endpoints while centralising monitoring.
- ✗
Create a shared SageMaker Model Registry across accounts and aggregate monitoring.
Why it's wrong here
A Model Registry stores model versions and approval metadata; it does not collect endpoint inference metrics, so no monitoring data is aggregated. Registries suit governing model lineage and deployment approval across accounts, not observing live endpoint performance, which requires per-account Model Monitor with centralised metric collection.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.