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MLA-C01 Practice Question: Your company uses SageMaker batch transform to…
Your company uses SageMaker batch transform to process a large dataset (5 TB) of customer transactions every night. The batch transform job uses a single ml.c5.4xlarge instance and takes about 6 hours to complete. However, the job recently started failing with an error message: 'Timed out waiting for transformation to complete. The maximum job duration is 3600 seconds.' You check the input data and notice that one of the input files is a single large JSON file of 50 GB, while the rest are smaller files. The job is configured with a batch strategy of 'MultiRecord' and a maximum payload size of 6 MB. What is the most likely cause of the timeout and which fix should you apply?
⚠ Common exam trap
AWS often tests the misconception that increasing instances or timeout alone can solve performance bottlenecks caused by a single large input file, when in fact SageMaker batch transform processes each file on a single instance and requires file-level splitting for parallelism.
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
✓
Split the large JSON file into smaller files (e.g., 100 MB each) before feeding to the batch transform job.
The batch transform job is timing out because the single 50 GB JSON file cannot be processed within the default 3600-second (1-hour) timeout. With a 'MultiRecord' batch strategy and a 6 MB maximum payload size, SageMaker must split the large file into many small batches, but the job still tries to read the entire file sequentially, causing excessive processing time. Splitting the large file into smaller files (e.g., 100 MB each) allows SageMaker to parallelize and complete the transform within the timeout.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the batch strategy to 'SingleRecord' so that each record is processed individually.
Why it's wrong here
SingleRecord would still load the entire 50 GB file as one request; it would not split it.
- ✓
Split the large JSON file into smaller files (e.g., 100 MB each) before feeding to the batch transform job.
Why this is correct
SageMaker batch transform splits input on file boundaries; small files allow parallel processing and stay within time limits.
- ✗
Increase the job timeout to 7200 seconds.
Why it's wrong here
This may allow the job to finish but will still be slow and risk hitting other limits; not a robust solution.
- ✗
Increase the number of instances to 5 in the batch transform job.
Why it's wrong here
Multiple instances will process different files, but if there is only one file, it will still be processed by a single instance, leading to the same issue.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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