- A
The S3 bucket is in a different region
Why wrong: Cross-region access would cause network errors, not CUDA memory.
- B
The batch size is too large for the GPU memory
Large batch sizes can exceed GPU memory, causing out-of-memory errors.
- C
The GPU driver is outdated
Why wrong: Outdated drivers typically cause compatibility errors, not out-of-memory.
- D
The training script has a memory leak on CPU
Why wrong: CPU memory leaks would not cause CUDA errors.
- E
The instance type does not have enough CPU cores
Why wrong: CUDA errors are GPU-specific, not CPU.
Question 1,416 of 1,755
Resolving CUDA_ERROR_OUT_OF_MEMORY in SageMaker Training by Adjusting Batch Size
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 using SageMaker to train a model with a custom algorithm. The training script uses TensorFlow and runs on GPU instances. The training job fails with 'CUDA_ERROR_OUT_OF_MEMORY'. What is the most likely cause?
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
✓
The batch size is too large for the GPU memory
The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.
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.
- ✗
The S3 bucket is in a different region
Why it's wrong here
Cross-region access would cause network errors, not CUDA memory.
- ✓
The batch size is too large for the GPU memory
Why this is correct
Large batch sizes can exceed GPU memory, causing out-of-memory errors.
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.
- ✗
The GPU driver is outdated
Why it's wrong here
Outdated drivers typically cause compatibility errors, not out-of-memory.
- ✗
The training script has a memory leak on CPU
Why it's wrong here
CPU memory leaks would not cause CUDA errors.
- ✗
The instance type does not have enough CPU cores
Why it's wrong here
CUDA errors are GPU-specific, not CPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that GPU errors are always driver-related, leading candidates to choose 'outdated GPU driver' instead of recognizing that the error message explicitly points to memory exhaustion, not driver version issues.
Detailed technical explanation
How to think about this question
Under the hood, TensorFlow allocates GPU memory for model parameters, activations, gradients, and optimizer states. The batch size multiplies the memory footprint of activations and gradients linearly; for example, doubling the batch size roughly doubles the memory required for intermediate tensors. A common real-world scenario is using a default batch size (e.g., 32 or 64) on a GPU with limited VRAM (e.g., 4 GB), which can cause this error, while reducing to 16 or 8 resolves it. The 'CUDA_ERROR_OUT_OF_MEMORY' is returned by the CUDA runtime API when cudaMalloc fails to allocate the requested 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
- →
Machine Learning Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
- →
Machine Learning Implementation and Operations practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: The batch size is too large for the GPU memory — The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.
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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jul 4, 2026
This MLS-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 MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.