The answer is to increase the instance type in the pipeline step configuration. This is correct because SageMaker Pipelines defines each training step with a specific instance type in the pipeline definition file, and upgrading that instance—for example, from ml.m5.large to ml.m5.xlarge or switching to a memory-optimized family like ml.r5—directly allocates more RAM to the training container without requiring any changes to your training script or algorithm. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of SageMaker Pipelines’ declarative infrastructure, where resource allocation is managed at the step level, not in code. A common trap is assuming you must rewrite the training logic or use Spot Instances, but the fix is purely a configuration change. Memory tip: think “step type, not script rewrite”—when a pipeline step runs out of memory, always check the instance type in the pipeline definition first.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the instance type in the pipeline step configuration
Option C is correct because SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.
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.
✗
Modify the training algorithm to use less memory
Why it's wrong here
This requires code changes and may not be straightforward.
✗
Reduce the batch size in the training script
Why it's wrong here
This requires modifying the training script.
✓
Increase the instance type in the pipeline step configuration
Why this is correct
Changing instance type is a configuration change, not a code change.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable managed spot training
Why it's wrong here
Spot training reduces cost but does not fix memory issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between infrastructure-level fixes (changing instance type in pipeline config) and code-level fixes (modifying script or algorithm), trapping candidates who think reducing batch size or enabling spot instances solves memory issues without considering the 'no code rewrite' constraint.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a PipelineDefinition with step configurations that include the `TrainingJobDefinition` or `TrainingStep`, where the `ResourceConfig` specifies the `InstanceType` and `InstanceCount`. Memory allocation is tied to the instance type; for example, ml.m5.large provides 8 GiB of memory, while ml.m5.xlarge provides 16 GiB. Increasing the instance type is a declarative change in the pipeline definition YAML or JSON, requiring no code changes in the training script. In real-world scenarios, teams often use parameterized pipeline definitions to dynamically adjust instance types based on dataset size or model complexity.
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 — 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: Increase the instance type in the pipeline step configuration — Option C is correct because SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.
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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Question Discussion
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