hardMultiple Select
MLA-C01 Practice Question: A team is deploying a model using SageMaker…
A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)
⚠ Common exam trap
Many candidates confuse the built-in ConditionStep with a Lambda-based custom step, or assume that pipeline failure triggers automatic retries, when in fact SageMaker Pipelines requires explicit retry policies and does not retry on condition failures.
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 evaluation step must output a JSON file in a specific format to be used by the condition step.
Option A is correct because SageMaker Pipelines' ConditionStep consumes a JSON property file produced by a preceding step (typically via a PropertyFile output), and that file must follow the supported JSON structure so the condition can parse and evaluate the metric value. Option B is correct because the condition can reference the accuracy value either through a pipeline parameter (e.g., a ParameterString/ParameterFloat passed in) or through the step's property file using JsonGet (e.g., JsonGet(step_name=eval_step, property_file='evaluation.json', json_path='metrics.accuracy')), which is exactly how the threshold comparison is expressed. Option C is incorrect because conditional branching in SageMaker Pipelines is natively handled by the ConditionStep, not by invoking a separate Lambda function. Option D is incorrect because a failing condition causes the pipeline to fail or take the defined branch; it does not trigger an automatic retry of the training step. Option E is incorrect because registering the model before evaluating the condition would defeat the purpose of gating registration on accuracy > 0.9.
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 must output a JSON file in a specific format to be used by the condition step.
Why this is correct
The condition step parses the evaluation step's output as JSON, so that file must follow SageMaker's expected property-file schema with metric names and values; otherwise the accuracy comparison cannot be evaluated and the branch fails.
- ✓
The condition step can reference the accuracy value using a pipeline parameter or property file.
Why this is correct
Condition steps read metrics either from a pipeline parameter or from a property file emitted by a previous step. Referencing the accuracy value this way lets the pipeline compare it against 0.9 and branch to registration or failure.
- ✗
The conditional step should be implemented as a separate Lambda function called from the pipeline.
Why it's wrong here
SageMaker Pipelines has a native Condition step that evaluates JSON metrics and branches or fails the pipeline directly; a Lambda function adds custom code and IAM plumbing without providing that branching. Lambda suits arbitrary logic outside pipeline primitives, such as calling external APIs between steps.
- ✗
The pipeline will automatically retry the training step if the condition fails.
Why it's wrong here
SageMaker Pipelines does not retry steps when a Condition step evaluates false; the pipeline fails, and retries occur only on step failure via RetryPolicy. Automatic retraining on poor accuracy would require a separate orchestration mechanism such as a Lambda trigger or EventBridge rule.
- ✗
The model registration step should be placed before the condition step to ensure the model is always registered.
Why it's wrong here
Registering before the condition defeats the gate: the model would be registered regardless of accuracy, so the accuracy > 0.9 requirement never blocks promotion. Registration belongs in the condition's true branch. Placing it earlier would suit pipelines that always register and merely tag metrics.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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