1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
An organization wants to fine-tune a large language model on OCI using their proprietary data. They are concerned about data privacy and want to ensure that fine-tuning data does not leave the OCI region. Which OCI service should they use to securely store and manage their training data?
⚠ Common exam trap
Oracle often tests the misconception that any storage service can be used for data residency, but the trap here is that Block Volume and File Storage are compute-attached services that do not inherently enforce regional data boundaries for data at rest across multiple services, while Object Storage is the only regional service designed for secure, scalable, and region-bound storage of unstructured data like LLM training datasets.
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 Object Storage
C is correct because OCI Object Storage is a regional service that stores data within a specific OCI region, ensuring that fine-tuning data does not leave that region. It provides secure, durable, and scalable storage for large datasets, such as training data for LLMs, with encryption at rest and in transit, and supports direct integration with OCI Data Science and Generative AI services for fine-tuning workflows.
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 Block Volume
Why it's wrong here
Block Volume is used for persistent block storage for compute instances, not for storing training datasets directly.
- ✗
OCI File Storage
Why it's wrong here
File Storage is suitable for shared file systems but not optimized for large-scale training data.
- ✓
OCI Object Storage
Why this is correct
Object Storage provides secure, regional storage ideal for large datasets.
- ✗
Oracle Autonomous Database
Why it's wrong here
Autonomous Database is for transactional and analytical workloads, not raw training data.
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