- A
Enable SageMaker Managed Spot Training to free memory
Why wrong: Spot training does not affect memory allocation; it only reduces cost.
- B
Implement data loading with multiprocessing and increase the number of workers
Why wrong: Multiprocessing may increase overall memory usage, potentially worsening the OOM issue.
- C
Reduce the batch size in the training script
Smaller batch sizes reduce memory consumption per step, helping to fit within the available RAM.
- D
Use SageMaker Pipe mode to stream data from S3
Why wrong: Pipe mode streams data directly from S3, which can reduce memory footprint, but the error may persist if the script still loads the full dataset.
Question 796 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is running a SageMaker training…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A data scientist is running a SageMaker training job with a custom PyTorch image. The training script loads a large dataset into memory, and the job fails with an out-of-memory error after a few minutes. The instance type is ml.m5.xlarge (16 GB RAM). What should the data scientist do to resolve this issue without changing the instance type?
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
✓
Reduce the batch size in the training script
Reducing the batch size decreases the amount of data loaded into memory at once, directly addressing the out-of-memory error without changing the instance type. Since the training script loads a large dataset into memory and fails after a few minutes, a smaller batch size reduces peak memory consumption per iteration, allowing the job to fit within the 16 GB RAM of ml.m5.xlarge.
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.
- ✗
Enable SageMaker Managed Spot Training to free memory
Why it's wrong here
Spot training does not affect memory allocation; it only reduces cost.
- ✗
Implement data loading with multiprocessing and increase the number of workers
Why it's wrong here
Multiprocessing may increase overall memory usage, potentially worsening the OOM issue.
- ✓
Reduce the batch size in the training script
Why this is correct
Smaller batch sizes reduce memory consumption per step, helping to fit within the available RAM.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Pipe mode to stream data from S3
Why it's wrong here
Pipe mode streams data directly from S3, which can reduce memory footprint, but the error may persist if the script still loads the full dataset.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse streaming data (Pipe mode) with reducing in-memory data loading, not realizing that the script's explicit load into memory bypasses any streaming benefit.
Detailed technical explanation
How to think about this question
Under the hood, PyTorch's DataLoader with multiprocessing uses shared memory or separate process memory, and increasing workers multiplies memory consumption by the number of workers times the batch size. Reducing batch size lowers the memory required for tensors, gradients, and optimizer states per iteration, which is critical when using a GPU or CPU with limited RAM. In real-world scenarios, this is a common first step for debugging OOM errors, often combined with gradient accumulation to maintain effective batch size without increasing memory.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Reduce the batch size in the training script — Reducing the batch size decreases the amount of data loaded into memory at once, directly addressing the out-of-memory error without changing the instance type. Since the training script loads a large dataset into memory and fails after a few minutes, a smaller batch size reduces peak memory consumption per iteration, allowing the job to fit within the 16 GB RAM of ml.m5.xlarge.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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