1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?
⚠ Common exam trap
Watch out — candidates often confuse OCI Functions (serverless) as suitable for long-running model inference, but its memory and timeout limits make it impractical for a 20GB model, while OCI Data Science Model Deployment is purpose-built for this scenario.
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
✓
OCI Data Science Model Deployment with OCI Monitoring and OCI Logging
OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI Compute with custom Docker container and Prometheus monitoring
Why it's wrong here
This requires manual setup for authentication, scaling, and monitoring, increasing operational overhead.
- ✓
OCI Data Science Model Deployment with OCI Monitoring and OCI Logging
Why this is correct
Model Deployment supports large models, authentication, and integrates with Monitoring and Logging for drift detection.
- ✗
OCI Functions with API Gateway for authentication
Why it's wrong here
OCI Functions have limited memory (max 2GB) and no native GPU support, unsuitable for a 20GB model.
- ✗
OCI Data Flow with OCI Data Catalog for model registry
Why it's wrong here
Data Flow is designed for batch Spark jobs, not real-time inference.
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