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.
Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The steps do not reference the Parameters; the values are hardcoded in the step definitions.
Option C is correct because the SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.
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.
✗
The pipeline encountered a runtime error and fell back to default values.
Why it's wrong here
No runtime error occurred.
✗
The parameter name has a typo; it should be 'TrainingInstanceCount' not 'TrainingInstanceCount'.
Why it's wrong here
The name matches, but it's unused.
✓
The steps do not reference the Parameters; the values are hardcoded in the step definitions.
Why this is correct
Parameters must be explicitly referenced in steps to take effect.
Related concept
Read the scenario before looking for a memorised answer.
✗
The training image is not compatible with the specified instance type.
Why it's wrong here
No indication of incompatibility.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that simply defining a parameter in the `Parameters` section automatically applies it to all steps, when in reality each step must explicitly reference the parameter using the `Parameters` object.
Detailed technical explanation
How to think about this question
In SageMaker Pipelines, parameters are defined in the `Parameters` section and must be referenced in step definitions using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). If a step hardcodes a value instead of referencing the parameter, the pipeline ignores the parameter and uses the hardcoded value. This is a common misconfiguration when migrating from static scripts to parameterized pipelines, as the parameter reference syntax is not automatically applied.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: The steps do not reference the Parameters; the values are hardcoded in the step definitions. — Option C is correct because the SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.
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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