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

1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question

Exhibit

{
  "data": {
    "id": "ocid1.finetuningjob.oc1.iad.xxxxx",
    "lifecycle-state": "FAILED",
    "lifecycle-details": "Job terminated due to out-of-memory error on worker node. Consider increasing the cluster shape or reducing the model size."
  }
}

Refer to the exhibit. A data scientist received this output after submitting a fine-tuning job. What is the most effective change to resolve the out-of-memory error?

⚠ Common exam trap

Oracle often tests the misconception that reducing epochs or learning rate can fix memory errors, when in fact memory errors are resource constraints that require scaling hardware (more nodes or GPUs) or reducing memory-intensive parameters like batch size or sequence length.

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

✓

Increase the number of nodes in the cluster.

The out-of-memory error during fine-tuning indicates that the model's memory requirements exceed the available resources on the current node. Increasing the number of nodes in the cluster distributes the model parameters, gradients, and optimizer states across multiple GPUs or nodes, effectively increasing the total memory capacity and resolving the OOM error. This is a standard approach in distributed training frameworks like PyTorch DDP or FSDP, which OCI Data Science supports.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Increase the sequence length.
Why it's wrong here
Incorrect: Increasing sequence length increases memory usage, worsening the problem.
✗
Reduce the learning rate.
Why it's wrong here
Incorrect: Learning rate affects training dynamics, not memory usage.
✗
Decrease the number of fine-tuning epochs.
Why it's wrong here
Incorrect: Fewer epochs reduce training time but do not solve the per-step memory issue.
✓
Increase the number of nodes in the cluster.
Why this is correct
Correct: More nodes mean more total memory, alleviating OOM.

Visual reference

Go deeper

1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question