- A
Condition step
Why wrong: Condition step controls branching but does not record metrics.
- B
Training step
Why wrong: Training step outputs a model artifact but does not record evaluation metrics.
- C
RegisterModel step
RegisterModel step registers the model and can include evaluation metrics as metadata.
- D
Processing step
Why wrong: Processing steps run custom code but do not inherently store metrics in the registry.
Question 278 of 1,000
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. 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 Pipelines with a Condition step to decide whether to register a model based on evaluation metrics. They want to also store the evaluation results for lineage tracking. Which step should they use to record the metrics?
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
✓
RegisterModel step
The RegisterModel step in SageMaker Pipelines is designed to create a model version in the SageMaker Model Registry, and it can accept metadata such as evaluation metrics via the `InferenceSpecification` or by passing a metrics dictionary. This allows the team to store evaluation results alongside the model for lineage tracking, fulfilling the requirement to record metrics after a Condition step approves registration.
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.
- ✗
Condition step
Why it's wrong here
Condition step controls branching but does not record metrics.
- ✗
Training step
Why it's wrong here
Training step outputs a model artifact but does not record evaluation metrics.
- ✓
RegisterModel step
Why this is correct
RegisterModel step registers the model and can include evaluation metrics as metadata.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Processing step
Why it's wrong here
Processing steps run custom code but do not inherently store metrics in the registry.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume the Condition step or Processing step can directly store metrics for lineage, but only the RegisterModel step can bind evaluation results to a model version in the Model Registry, which is the explicit requirement for lineage tracking.
Trap categories for this question
Command / output trap
Training step outputs a model artifact but does not record evaluation metrics.
Detailed technical explanation
How to think about this question
Under the hood, the RegisterModel step uses the `CreateModelPackage` API to create a model package in the Model Registry, where you can attach metadata such as `CustomerMetadataProperties` or `InferenceSpecification` to store evaluation metrics. A subtle behavior is that the metrics must be passed as a dictionary to the step's `model_metrics` parameter (using `MetricsSource` with a S3 path to a JSON file), not as inline values. In a real-world scenario, a team might run a batch evaluation in a Processing step, then use a Condition step to check if accuracy > 0.95, and only then pass the metrics S3 URI to the RegisterModel step to ensure only high-quality models are registered with their evaluation results.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: RegisterModel step — The RegisterModel step in SageMaker Pipelines is designed to create a model version in the SageMaker Model Registry, and it can accept metadata such as evaluation metrics via the `InferenceSpecification` or by passing a metrics dictionary. This allows the team to store evaluation results alongside the model for lineage tracking, fulfilling the requirement to record metrics after a Condition step approves registration.
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
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.
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