MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 does not address data exposure for monitoring.
✗
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
Replicating data may violate data compliance policies.
✓
Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability.
Why this is correct
This allows centralized monitoring without moving data across accounts.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a shared SageMaker Model Registry across accounts and aggregate monitoring.
Why it's wrong here
Model Registry holds model metadata, not monitoring metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing data replication (which exposes raw data) with metric aggregation (which exposes only statistical summaries), leading candidates to pick Option B despite its compliance violation.
Detailed technical explanation
How to think about this question
Cross-account CloudWatch observability uses a monitoring account that can view metrics, logs, and traces from source accounts via a CloudWatch cross-account observability dashboard, without moving the underlying data. SageMaker Model Monitor runs as a scheduled processing job within each account, analyzing inference data against a baseline and emitting custom metrics (e.g., feature distribution distances) to CloudWatch. This architecture is common in multi-account healthcare deployments where data sovereignty must be preserved while enabling centralized oversight.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability. — Option C is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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