Question 704 of 991

Deploying and Managing Generative AI on OCI →hardMultiple ChoiceObjective-mapped

Resolving GPU Out-of-Memory Errors in OCI Data Science Fine-Tuning

This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. 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 fine-tuning a generative AI model on OCI Data Science using a custom container with GPU resources. The training job fails with an out-of-memory error despite the GPU instance having sufficient memory. The job works fine on a smaller dataset. 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.

A
The training script has a memory leak
Why wrong: Memory leak would affect small dataset as well.
B
The GPU instance is not supported by OCI Data Science
Why wrong: GPU instances are supported.
C
The model is not compatible with the PyTorch version
Why wrong: Compatibility would cause errors regardless of dataset size.
D
The batch size is too large for the GPU memory
Large batch size can cause OOM errors; reducing batch size resolves it.

Full breakdown with real-world context →

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 most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.

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 training script has a memory leak
Why it's wrong here
Memory leak would affect small dataset as well.
✗
The GPU instance is not supported by OCI Data Science
Why it's wrong here
GPU instances are supported.
✗
The model is not compatible with the PyTorch version
Why it's wrong here
Compatibility would cause errors regardless of dataset size.
✓
The batch size is too large for the GPU memory
Why this is correct
Large batch size can cause OOM errors; reducing batch size resolves it.
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Oracle often tests the misconception that 'sufficient instance memory' guarantees no OOM errors, ignoring that GPU memory is a separate, finite resource that must accommodate both the model and the batch data simultaneously.

Detailed technical explanation

How to think about this question

Under the hood, GPU memory is shared between model parameters, gradients, optimizer states, and the input batch. For large models (e.g., LLaMA-2-7B), even a single batch can consume tens of GB of VRAM. Techniques like gradient accumulation or mixed-precision training (e.g., using `torch.cuda.amp`) can reduce memory pressure, but the fundamental issue is that the batch size multiplied by the per-sample memory footprint exceeds the GPU's VRAM capacity. In practice, data scientists often use `torch.cuda.max_memory_allocated()` to monitor peak usage and tune batch size accordingly.

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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.

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 1Z0-1127 question test?

Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — 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 most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.

What should I do if I get this 1Z0-1127 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.

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