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 data scientist needs to version and manage multiple models for a team of five. The team frequently experiments with different algorithms and hyperparameters. They need a centralized registry to store, deploy, and compare model versions. Which AWS service should the data scientist use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Model Registry.
Amazon SageMaker Model Registry is the correct choice because it provides a centralized repository specifically designed for cataloging, versioning, approving, and deploying machine learning models. It integrates natively with SageMaker pipelines and endpoints, enabling the team to compare model versions, manage metadata (e.g., hyperparameters, metrics), and promote models through stages (e.g., from staging to production) with approval workflows.
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.
✗
Store each model artifact in Amazon S3 with manual versioning in the key name.
Why it's wrong here
This is error-prone and lacks metadata management; SageMaker Model Registry provides structured versioning.
✗
Use AWS Config to track model version changes.
Why it's wrong here
AWS Config records resource configuration changes, not model versions.
✗
Use AWS CodeArtifact to store model packages.
Why it's wrong here
CodeArtifact is for artifacts like Maven or npm packages; it does not provide model-specific metadata.
✓
Use Amazon SageMaker Model Registry.
Why this is correct
Model Registry provides centralized version control, metadata, and stage transitions (Draft, Approved, Deployed).
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse AWS CodeArtifact (a package manager for code libraries) with a model registry, overlooking that SageMaker Model Registry is purpose-built for ML model versioning, metadata tracking, and deployment orchestration.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Registry stores model versions as 'model packages' in a registry, each linked to an S3 artifact and a set of metadata (e.g., training job ARN, evaluation metrics). The registry supports a multi-stage lifecycle (e.g., 'Pending', 'Approved', 'Rejected'), which can be automated via SageMaker Pipelines or CI/CD tools like AWS CodePipeline. In a real-world scenario, a team can use the registry to compare metrics (e.g., accuracy, latency) across versions and automatically deploy an approved model to a SageMaker endpoint using a single API call.
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 Amazon SageMaker Model Registry. — Amazon SageMaker Model Registry is the correct choice because it provides a centralized repository specifically designed for cataloging, versioning, approving, and deploying machine learning models. It integrates natively with SageMaker pipelines and endpoints, enabling the team to compare model versions, manage metadata (e.g., hyperparameters, metrics), and promote models through stages (e.g., from staging to production) with approval workflows.
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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