Question 709 of 1,000
Deployment and Orchestration of ML WorkflowsmediumMultiple ChoiceObjective-mapped

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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 to automate retraining. They want to skip the training step if the data has not changed since the last run. Which feature should they enable?

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

Step caching

Step caching in SageMaker Pipelines allows you to reuse the output from a previous execution of a step if its input data and configuration parameters have not changed. By enabling caching on the training step, the pipeline automatically skips re-executing that step when the data is identical, saving time and cost. This directly addresses the requirement to skip retraining when data has not changed.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the distinction between step caching (automatic, built-in) and a Condition step (manual, custom logic), leading candidates to overthink and choose the more complex option D when the simpler caching feature is the correct answer.

Detailed technical explanation

How to think about this question

Step caching works by hashing the step's input data (e.g., S3 object ETags or dataset checksums) along with its configuration parameters and the step type. When a new pipeline execution starts, SageMaker compares this hash against previous executions; if a match is found, the cached output is reused without rerunning the step. This is particularly useful in production pipelines where training data is updated infrequently, as it avoids unnecessary compute costs and reduces pipeline execution time.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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: Step caching — Step caching in SageMaker Pipelines allows you to reuse the output from a previous execution of a step if its input data and configuration parameters have not changed. By enabling caching on the training step, the pipeline automatically skips re-executing that step when the data is identical, saving time and cost. This directly addresses the requirement to skip retraining when data has not changed.

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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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.