mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Refer to the exhibit
Exhibit
{
"PipelineExperimentConfig": {
"ExperimentName": "my-experiment",
"TrialName": "my-trial"
},
"Parameters": {
"TrainingInstanceType": "ml.m5.large",
"TrainingInstanceCount": 2,
"MaxRuntimeInSeconds": 86400
},
"Steps": [
{
"Name": "Preprocess",
"Type": "Processing",
"ProcessingJobName": "preprocess-job",
"ProcessingResources": {
"ClusterConfig": {
"InstanceCount": 1,
"InstanceType": "ml.m5.large",
"VolumeSizeInGB": 30
}
}
},
{
"Name": "Train",
"Type": "Training",
"TrainingJobName": "train-job",
"AlgorithmSpecification": {
"TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-algo:latest",
"TrainingInputMode": "File"
},
"ResourceConfig": {
"InstanceCount": 2,
"InstanceType": "ml.m5.large",
"VolumeSizeInGB": 30
}
}
]
}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?
⚠ Common exam trap
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.
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
✓
The steps do not reference the Parameters; the values are hardcoded in the step definitions.
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.
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.
- ✗
The training image is not compatible with the specified instance type.
Why it's wrong here
No indication of incompatibility.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.