Courseiva
Machine Learning Implementation and OperationshardMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

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?

⚠ Common exam trap

AWS often tests the misconception that GPU errors are always driver-related, leading candidates to choose 'outdated GPU driver' instead of recognizing that the error message explicitly points to memory exhaustion, not driver version issues.

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.

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.

  • The GPU driver is outdated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    Outdated drivers typically cause compatibility errors, not out-of-memory.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.

About these practice questions

Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.