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MLA-C01 Practice Question: Using an Amazon SageMaker pipeline for automated…
A company is using an Amazon SageMaker pipeline for automated retraining. The pipeline fails intermittently due to transient errors in the training job. Which steps should the team take to ensure the pipeline completes successfully? (Choose THREE.)
⚠ Common exam trap
Watch out — candidates often confuse 'checkpointing' (which enables resumption after interruption) with 'retry logic' (which re-runs the step on failure), and fail to recognize that both are needed together to handle transient errors in a SageMaker pipeline.
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
✓
Enable managed spot training for cost savings and use checkpointing to resume from interruptions.
Enabling managed spot training with checkpointing allows the training job to resume from the last saved state if it is interrupted due to spot instance reclaimation. This directly addresses transient errors by providing fault tolerance, ensuring the pipeline can complete even if the underlying compute is preempted.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable managed spot training for cost savings and use checkpointing to resume from interruptions.
Why this is correct
Spot instances can be interrupted; checkpointing helps.
- ✗
Use a larger instance type for the training job to reduce the chance of failure.
Why it's wrong here
Instance size does not address transient errors.
- ✗
Implement automatic model checkpointing by setting the CheckpointConfig in the pipeline step.
Why it's wrong here
SageMaker Pipeline does not automatically use checkpoints on retry; you need custom logic.
- ✓
Configure the SageMaker pipeline step to retry on failure with a maximum number of attempts.
Why this is correct
Retries can handle transient errors.
- ✓
Add exponential backoff in any custom Python code that makes API calls to AWS services.
Why this is correct
Reduces throttling errors.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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