- A
Write an AWS Lambda function to copy the best model to a specific S3 prefix.
Why wrong: Lambda can automate but does not provide model governance features.
- B
Manually download the best model artifact and upload to S3, then create a model in SageMaker.
Why wrong: Manual process lacks provenance and automation.
- C
Use the SageMaker Model Registry's create_model_package_from_estimator or equivalent API to register the model.
Model Registry captures artifacts, metrics, and supports approval workflow.
- D
Use Experiment analytics to view results and then create a model package using the Run's artifact URI.
Why wrong: While possible, it is not the integrated workflow; Model Registry is designed for this.
Question 582 of 1,000
easyMultiple ChoiceObjective-mapped
Registering Models from SageMaker Experiments to Model Registry — AWS Certified ML Engineer Associate Explained
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team uses SageMaker Experiments to track multiple training runs. They need to register the best-performing model in the model registry for approval. Which method ensures the model artifacts and metadata are captured correctly?
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 the SageMaker Model Registry's create_model_package_from_estimator or equivalent API to register the model.
Option C is correct because the `create_model_package_from_estimator` API (or equivalent `register_model` in the SageMaker SDK) automatically captures the trained model artifacts, training metadata, hyperparameters, and metrics from a SageMaker Experiment run and registers them as a versioned model package in the Model Registry. This ensures that the model is stored with all necessary provenance for approval workflows, without manual steps or risk of metadata loss.
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.
- ✗
Write an AWS Lambda function to copy the best model to a specific S3 prefix.
Why it's wrong here
Lambda can automate but does not provide model governance features.
- ✗
Manually download the best model artifact and upload to S3, then create a model in SageMaker.
Why it's wrong here
Manual process lacks provenance and automation.
- ✓
Use the SageMaker Model Registry's create_model_package_from_estimator or equivalent API to register the model.
Why this is correct
Model Registry captures artifacts, metrics, and supports approval workflow.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Experiment analytics to view results and then create a model package using the Run's artifact URI.
Why it's wrong here
While possible, it is not the integrated workflow; Model Registry is designed for this.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse simply identifying the best run via Experiment analytics (Option D) with the automated registration process, overlooking that the AWS API in Option C is the only method that guarantees complete metadata capture and versioning in the SageMaker Model Registry.
Detailed technical explanation
How to think about this question
Under the hood, `create_model_package_from_estimator` calls the SageMaker `CreateModelPackage` API and populates the `InferenceSpecification` with the model artifact S3 URI, the training image, and optionally a `MetadataProperties` object that ties the model package to the specific training job ARN. This linkage is critical for downstream approval workflows and lineage tracking in SageMaker Experiments, as it allows auditors to trace a registered model back to its exact training run, hyperparameters, and evaluation metrics.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use the SageMaker Model Registry's create_model_package_from_estimator or equivalent API to register the model. — Option C is correct because the `create_model_package_from_estimator` API (or equivalent `register_model` in the SageMaker SDK) automatically captures the trained model artifacts, training metadata, hyperparameters, and metrics from a SageMaker Experiment run and registers them as a versioned model package in the Model Registry. This ensures that the model is stored with all necessary provenance for approval workflows, without manual steps or risk of metadata loss.
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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Last reviewed: Jul 4, 2026
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