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MLA-C01 Practice Question: A company uses Amazon SageMaker Pipelines for…
A company uses Amazon SageMaker Pipelines for automated retraining. The pipeline includes a processing step that runs a Python script. The script uses the boto3 library to call an AWS service, but the calls are being throttled. What is the MOST effective way to address this within the pipeline?
⚠ Common exam trap
Test-takers frequently confuse infrastructure-level scaling (increasing instance count) with application-level retry logic, assuming more instances will reduce API call frequency, when in fact each instance independently makes the same number of calls and can still be throttled.
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
✓
Modify the Python script to include retry with exponential backoff when receiving throttling exceptions.
Implementing retry with exponential backoff directly in the Python script is the most effective way to handle transient throttling exceptions from AWS service API calls. This approach is a best practice for managing service limits within a SageMaker Pipeline processing step, as it allows the script to automatically recover from throttling without modifying the pipeline structure or requiring manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the instance count for the processing step to distribute the API calls.
Why it's wrong here
Adding instances parallelises the script, multiplying concurrent API calls and worsening throttling rather than reducing request rate. It is tempting because horizontal scaling speeds up processing generally, and would be correct for CPU-bound workloads, but throttling is a per-account request-rate limit that more instances cannot raise.
- ✓
Modify the Python script to include retry with exponential backoff when receiving throttling exceptions.
Why this is correct
Retry with exponential backoff directly addresses throttling by spacing repeated boto3 calls, letting the service recover between attempts. This satisfies the stem's constraint that throttling occurs inside the processing step's Python script, where the pipeline's own retry policy cannot intercept individual API calls. Increasing provisioned throughput or pipeline retries would not resolve per-call rate limiting.
- ✗
Request a service quota increase for the throttling limit.
Why it's wrong here
Quota increases raise the ceiling on API calls but do not stop throttling caused by the script issuing requests faster than permitted. It tempts because throttling errors mention limits. The pipeline should instead add retry logic with exponential backoff, or batch and pace the boto3 calls within the processing step.
- ✗
Add a wait step in the pipeline before the processing step.
Why it's wrong here
A wait step delays the whole pipeline once, then the script still fires its API calls at the same rate and gets throttled again. It is tempting because pausing looks like it eases pressure, and would suit a dependency-timing issue, but throttling needs per-request retry with exponential backoff and jitter inside the script.
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