1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data science team is using OCI Data Science to fine-tune a model. They notice that training jobs are failing due to out-of-memory errors on the notebook session. What should they do to resolve this?
⚠ Common exam trap
Oracle often tests the misconception that autoscaling or reducing batch size can fix memory issues in a single-node notebook session, but the correct approach is to match the compute shape to the workload's memory requirements.
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
✓
Switch to a larger notebook session shape.
Out-of-memory errors during training on a notebook session indicate that the current shape's memory capacity is insufficient for the model or data being processed. Switching to a larger notebook session shape directly increases available RAM and compute resources, resolving the memory constraint without altering the training logic or infrastructure type.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable autoscaling on the notebook session.
Why it's wrong here
Autoscaling is for endpoints, not notebook sessions.
- ✗
Use OCI Data Flow instead.
Why it's wrong here
Data Flow is for Spark jobs, not for fine-tuning LLMs.
- ✓
Switch to a larger notebook session shape.
Why this is correct
A larger shape provides more memory, resolving OOM issues.
- ✗
Reduce the batch size in the training script.
Why it's wrong here
While reducing batch size can help, it may not fully resolve OOM and may impact training efficiency; increasing shape is more direct.
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