- A
Training step
Training outputs a model artifact; caching avoids retraining if inputs unchanged.
- B
Transform step (if used)
Why wrong: Not all pipelines include a transform step; caching on Transform is possible but not required for the described scenario.
- C
Processing step (evaluation)
Processing evaluation produces metrics; caching avoids re-evaluation if inputs unchanged.
- D
Condition step
Why wrong: Condition step is a logic branch; it does not produce cacheable outputs.
- E
RegisterModel step
RegisterModel can cache if the input model artifact and metadata are unchanged, preventing redundant registration.
Question 414 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 machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)
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
✓
Training step
For caching to avoid redundant runs, the steps that produce outputs that can be reused must have caching enabled. The processing step (evaluation) and training step both generate outputs that can be cached if their inputs (code, data, hyperparameters) remain the same. The condition step does not produce outputs to cache; it just branches. The RegisterModel step typically registers metadata, but its inputs (model artifact, metrics) may be generated by previous steps; enabling caching on the RegisterModel step can also avoid re-running if the same model artifact is already registered.
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.
- ✓
Training step
Why this is correct
Training outputs a model artifact; caching avoids retraining if inputs unchanged.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Transform step (if used)
Why it's wrong here
Not all pipelines include a transform step; caching on Transform is possible but not required for the described scenario.
- ✓
Processing step (evaluation)
Why this is correct
Processing evaluation produces metrics; caching avoids re-evaluation if inputs unchanged.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Condition step
Why it's wrong here
Condition step is a logic branch; it does not produce cacheable outputs.
- ✓
RegisterModel step
Why this is correct
RegisterModel can cache if the input model artifact and metadata are unchanged, preventing redundant registration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Condition step is a logic branch; it does not produce cacheable outputs.
Scenario analysis trap
Not all pipelines include a transform step; caching on Transform is possible but not required for the described scenario.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Training step — For caching to avoid redundant runs, the steps that produce outputs that can be reused must have caching enabled. The processing step (evaluation) and training step both generate outputs that can be cached if their inputs (code, data, hyperparameters) remain the same. The condition step does not produce outputs to cache; it just branches. The RegisterModel step typically registers metadata, but its inputs (model artifact, metrics) may be generated by previous steps; enabling caching on the RegisterModel step can also avoid re-running if the same model artifact is already registered.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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