- A
Reduce the batch size
Smaller batch size reduces memory usage.
- B
Use gradient accumulation to simulate a larger batch size
Why wrong: Gradient accumulation does not reduce memory per step.
- C
Use model parallelism across multiple GPUs
Why wrong: Model parallelism is more complex and may not be needed.
- D
Enable automatic mixed precision (AMP)
Why wrong: AMP is already in use.
- E
Increase the instance type to ml.p3.8xlarge
Why wrong: This increases cost and is not the only solution.
Question 202 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 training a deep learning model on Amazon SageMaker using a custom TensorFlow container. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 16 GB GPU memory and 61 GB system memory. The model uses mixed precision training. Which step should the data scientist take to resolve the 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
Reducing the batch size directly decreases the memory footprint per training step, which is the most straightforward way to resolve an OutOfMemory error without changing the instance type. Since the model already uses mixed precision training (which reduces memory usage via FP16), the remaining memory pressure is likely from the batch size being too large for the 16 GB GPU memory on the ml.p3.2xlarge instance.
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.
- ✓
Reduce the batch size
Why this is correct
Smaller batch size reduces memory usage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use gradient accumulation to simulate a larger batch size
Why it's wrong here
Gradient accumulation does not reduce memory per step.
- ✗
Use model parallelism across multiple GPUs
Why it's wrong here
Model parallelism is more complex and may not be needed.
- ✗
Enable automatic mixed precision (AMP)
Why it's wrong here
AMP is already in use.
- ✗
Increase the instance type to ml.p3.8xlarge
Why it's wrong here
This increases cost and is not the only solution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse gradient accumulation (Option B) as a memory-saving technique, but it actually increases memory usage per step because it stores gradients across multiple micro-batches, whereas reducing batch size directly lowers peak memory consumption.
Detailed technical explanation
How to think about this question
Under the hood, GPU memory is consumed by activations, gradients, optimizer states, and model parameters. Mixed precision (FP16) halves the memory for activations and gradients, but the batch size directly determines the number of activations stored during forward pass. Reducing batch size by a factor of 2 can cut peak memory usage by roughly 40-50%, often resolving OOM without needing to alter the model architecture or training strategy. In practice, a batch size of 32 on a 16 GB GPU might OOM, while 16 works fine.
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.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — 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 — Reducing the batch size directly decreases the memory footprint per training step, which is the most straightforward way to resolve an OutOfMemory error without changing the instance type. Since the model already uses mixed precision training (which reduces memory usage via FP16), the remaining memory pressure is likely from the batch size being too large for the 16 GB GPU memory on the ml.p3.2xlarge instance.
What should I do if I get this MLS-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
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