- A
Set the batch strategy to 'SingleRecord' so that each record is processed individually.
Why wrong: SingleRecord would still load the entire 50 GB file as one request; it would not split it.
- B
Split the large JSON file into smaller files (e.g., 100 MB each) before feeding to the batch transform job.
SageMaker batch transform splits input on file boundaries; small files allow parallel processing and stay within time limits.
- C
Increase the job timeout to 7200 seconds.
Why wrong: This may allow the job to finish but will still be slow and risk hitting other limits; not a robust solution.
- D
Increase the number of instances to 5 in the batch transform job.
Why wrong: 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.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
SageMaker batch transform processes input files as atomic units; a single large file is assigned to one instance regardless of the instance count. The 'MultiRecord' batch strategy splits the file into records based on the maximum payload size (6 MB), but the file must still be read entirely by that instance, leading to high I/O and processing time. In practice, splitting large files into chunks of 100–200 MB ensures balanced distribution across instances and avoids hitting the default 3600-second timeout, which is a hard limit for synchronous batch transform jobs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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