- A
Enable pipeline caching on the training step
Why wrong: Caching reuses the output if the step configuration (inputs, parameters, code) is identical; it does not detect unchanged data if input paths differ.
- B
Use a Lambda step to check data before running the training step
Why wrong: A Lambda step can check the data but cannot conditionally skip the next step; ConditionStep is needed for branching.
- C
Use a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged
This allows skipping the training step dynamically based on data content changes.
- D
Set the training step's CacheConfig with a TTL of 24 hours
Why wrong: TTL-based caching still runs the step if the TTL expires, even if data hasn't changed.
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 retrain a model nightly. They want to skip the training step if the new data is unchanged (same checksum as previous run) to save cost and time. Which pipeline configuration achieves this?
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 a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged
Option C is correct because SageMaker Pipelines' ConditionStep allows you to evaluate a condition—such as comparing the current data checksum to a stored previous checksum—and branch accordingly. If the checksums match, you can route to a NoOp step (which does nothing) instead of executing the training step, thereby skipping the training and saving cost and time. This is the native, recommended pattern for conditional execution in SageMaker Pipelines.
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.
- ✗
Enable pipeline caching on the training step
Why it's wrong here
Caching reuses the output if the step configuration (inputs, parameters, code) is identical; it does not detect unchanged data if input paths differ.
- ✗
Use a Lambda step to check data before running the training step
Why it's wrong here
A Lambda step can check the data but cannot conditionally skip the next step; ConditionStep is needed for branching.
- ✓
Use a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged
Why this is correct
This allows skipping the training step dynamically based on data content changes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the training step's CacheConfig with a TTL of 24 hours
Why it's wrong here
TTL-based caching still runs the step if the TTL expires, even if data hasn't changed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse pipeline caching (which caches based on step input parameters) with conditional branching based on external data state, leading them to pick Option A or D, which do not actually evaluate data checksums.
Trap categories for this question
Command / output trap
Caching reuses the output if the step configuration (inputs, parameters, code) is identical; it does not detect unchanged data if input paths differ.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Pipelines uses a directed acyclic graph (DAG) where each step is a node. The ConditionStep evaluates a Boolean expression (e.g., using a JsonGet or Equals condition) and then routes execution to one of two branches. The NoOp step is a lightweight step that performs no compute, so it incurs no cost and completes almost instantly. In a real-world scenario, you would store the previous checksum in a SageMaker Parameter Store or Amazon S3, and the ConditionStep would compare it to the current checksum computed in a prior processing step.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Deployment and Orchestration of ML Workflows — study guide chapter
Learn the concepts, then practise the questions
- →
Deployment and Orchestration of ML Workflows practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged — Option C is correct because SageMaker Pipelines' ConditionStep allows you to evaluate a condition—such as comparing the current data checksum to a stored previous checksum—and branch accordingly. If the checksums match, you can route to a NoOp step (which does nothing) instead of executing the training step, thereby skipping the training and saving cost and time. This is the native, recommended pattern for conditional execution in SageMaker Pipelines.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.