easyMultiple Choice
MLA-C01 Practice Question: A data scientist needs to version and manage…
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?
⚠ Common exam trap
Candidates often 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.
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 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.
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
Manual S3 key naming provides no model lineage, metrics, approval workflow or deployment integration, so comparing versions and deploying them is unsupported. It is tempting because S3 is durable, cheap storage and would suit archiving raw artefacts, but the stem requires a centralised registry service.
- ✗
Use AWS Config to track model version changes.
Why it's wrong here
AWS Config records and evaluates resource configuration changes against rules; it neither stores model artefacts nor deploys them. It is tempting because it tracks changes over time, and it would suit auditing whether an S3 bucket or endpoint configuration drifted, but the stem requires model versioning, comparison and deployment.
- ✗
Use AWS CodeArtifact to store model packages.
Why it's wrong here
CodeArtifact stores software package artefacts such as npm, Maven and Python libraries, not trained models with lineage, metrics or deployment endpoints. It is tempting because it is a managed artefact repository, and it would suit distributing a shared code library, but the stem requires a model registry with version comparison and deployment.
- ✓
Use Amazon SageMaker Model Registry.
Why this is correct
Amazon SageMaker Model Registry provides a centralised catalogue for versioning, comparing and approving models, and integrates with deployment endpoints. It satisfies the stem's need for one shared registry across five team members experimenting with algorithms and hyperparameters, unlike per-notebook or S3-only storage.
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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.