1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A data science team at a healthcare company has fine-tuned a Llama 2 model using OCI Data Science and registered it in the Model Catalog. They want to deploy it as a managed endpoint using OCI Generative AI. The model requires 64 GB of GPU memory. The team has created a dedicated AI cluster with a single node shape that has 48 GB GPU memory. When they attempt to deploy the model, the deployment fails with an error indicating insufficient resources. The team has verified that the model artifact is correct and that the compartment policies allow deployment. What should the team do to successfully deploy the model?
⚠ Common exam trap
A common mix-up: candidates think adding more nodes or enabling model parallelism can aggregate GPU memory, but OCI Generative AI managed endpoints do not support distributed inference across nodes, so the only valid solution is to use a node shape with sufficient single-GPU memory.
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
✓
Select a node shape with higher GPU memory, such as 80 GB.
The model requires 64 GB of GPU memory, but the dedicated AI cluster uses a node shape with only 48 GB. The only way to satisfy the memory requirement is to select a node shape with higher GPU memory, such as 80 GB, as OCI Generative AI managed endpoints require a single node to host the entire model. Increasing nodes or enabling model parallelism does not help because OCI Generative AI does not support distributed inference across nodes for managed endpoints, and reducing precision may not guarantee the model fits or may degrade accuracy.
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 number of nodes in the cluster to 2.
Why it's wrong here
More nodes do not increase per-node memory; the model still cannot fit on a single node.
- ✗
Enable model parallelism to split the model across nodes.
Why it's wrong here
Model parallelism requires multiple nodes and configuration; not immediately available.
- ✓
Select a node shape with higher GPU memory, such as 80 GB.
Why this is correct
Using a node shape with sufficient memory allows the model to be loaded.
- ✗
Reduce the model's precision from FP16 to INT8 to lower memory usage.
Why it's wrong here
Reducing model precision to INT8 would indeed decrease memory requirements, making it tempting as a solution for resource constraints. However, this specific scenario involves a Llama 2 model fine-tuned on OCI Data Science and deployed via OCI Generative AI, which currently does not support INT8 precision for model deployment, thus failing to resolve the insufficient GPU memory error.
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