1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A machine learning engineer is deploying a fine-tuned Llama 2 model on OCI Data Science model deployment. The deployment fails with an error: 'Model artifact exceeds the maximum allowed size of 10 GB.' The model files total 12 GB. What is the best approach to resolve this?
⚠ Common exam trap
Oracle often tests the misconception that you can increase service limits via a support ticket, but for model artifact size, the limit is architectural and not adjustable; candidates may also incorrectly assume compression solves the issue without considering decompression at runtime.
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
✓
Store the model in Object Storage and reference it in the deployment configuration
OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store the model in Object Storage and reference it in the deployment configuration
Why this is correct
Object Storage allows large models and is supported by model deployment.
- ✗
Use a different model that is smaller than 10 GB
Why it's wrong here
Not a solution if the specific model is required.
- ✗
Increase the model deployment artifact size limit via a service request
Why it's wrong here
The limit is fixed and cannot be increased.
- ✗
Compress the model artifact to under 10 GB using gzip
Why it's wrong here
Compression may not achieve <10 GB and deployment may not support compressed format.
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