hardMultiple Choice
MLA-C01 Practice Question: A team is using SageMaker Pipelines to train a…
A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evaluation, and registration. They use a Condition step to evaluate the model's accuracy and if it exceeds a threshold, register the model. They run the pipeline and the training step succeeds, but the pipeline fails at the Condition step with an error: 'Unable to evaluate condition: the property 'Accuracy' does not exist.' The evaluation step output is a JSON file with key 'accuracy'. What is the most likely cause?
⚠ Common exam trap
AWS often tests the subtlety of case sensitivity in property names when referencing step outputs in SageMaker Pipelines, leading candidates to incorrectly assume the evaluation step failed or that the pipeline definition has a syntax error.
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 Condition step is referencing the wrong property name.
The Condition step in SageMaker Pipelines evaluates a property from the output of a previous step. The error 'Unable to evaluate condition: the property 'Accuracy' does not exist' indicates that the Condition step is looking for a property named 'Accuracy' (capital A), but the evaluation step outputs a JSON file with the key 'accuracy' (lowercase a). This mismatch in property name casing causes the condition to fail, even though the evaluation step produced the correct output.
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 evaluation step did not produce the output correctly.
Why it's wrong here
The error names a missing property, not a malformed file, so the evaluation step's JSON output evidently exists and parses; the Condition step is simply referencing the wrong property name or path. It is tempting because a genuinely failed evaluation step would also break the pipeline, making this the obvious first suspect.
- ✗
The training step output is being used instead of the evaluation step output.
Why it's wrong here
The training step emits model artefacts, not an accuracy metric, so referencing its output would produce exactly this missing-property error; the Condition step must read the evaluation step's JSON. It is tempting because the training step did succeed, and its output is the pipeline's most prominent artefact.
- ✗
The pipeline definition has a syntax error.
Why it's wrong here
A syntax error would fail pipeline validation at definition or start time, not at runtime with a property-resolution message naming 'Accuracy'. It is tempting because YAML or JSON mistakes are common in pipeline definitions, so a malformed definition would plausibly be the first thing suspected.
- ✓
The Condition step is referencing the wrong property name.
Why this is correct
The evaluation step writes JSON keyed 'accuracy' (lowercase), but the Condition step queries 'Accuracy'. SageMaker property references are case-sensitive, so the mismatch produces the 'property does not exist' error; aligning the property name resolves it.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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