AI0-001 AI Implementation and Operations Practice Question
A data scientist submits a model training job to a cloud ML platform. The job fails with an error: "Out of memory: Killed process." The training code is proven to work on the developer's local machine with 16GB RAM. The cloud instance has 32GB RAM. What is the most likely cause?
⚠ Common exam trap
CompTIA often tests the misconception that 'more RAM always solves memory errors', but the trap here is that the error is caused by inefficient data loading (no batching) rather than absolute memory size, so candidates must recognize the OOM killer message as a symptom of memory exhaustion from unbounded data consumption.
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 training code is loading the entire dataset into memory without batch processing
The error 'Out of memory: Killed process' occurs when the operating system's OOM killer terminates a process that exceeds available RAM. Even though the cloud instance has 32GB RAM, if the training code loads the entire dataset into memory without batch processing (e.g., using pandas.read_csv() without chunking), it can consume far more memory than the dataset size due to Python object overhead, temporary copies, and intermediate tensors. The local machine with 16GB RAM may have worked due to a smaller dataset or different memory pressure, but the cloud instance's 32GB is insufficient if the code is not memory-efficient.
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 cloud instance does not have enough memory
Why it's wrong here
32GB > 16GB, so memory should be sufficient unless other processes consume memory.
- ✓
The training code is loading the entire dataset into memory without batch processing
Why this is correct
If the dataset is larger than memory, the process may exhaust RAM even if instances have more memory than local.
- ✗
The training model is too large for the GPU memory
Why it's wrong here
The error is from the system killing a process, not GPU-related.
- ✗
The cloud instance ran out of disk space
Why it's wrong here
The error is about memory, not disk space.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.