1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data engineer wants to migrate a large corpus of PDFs to OCI for use with GenAI. Which storage and preprocessing approach is most efficient for RAG?
⚠ Common exam trap
Oracle often tests the misconception that any storage service (like File Storage or Database) can be used for RAG, but the key is that Object Storage combined with a managed AI extraction service is the most efficient for unstructured data at scale, avoiding local processing overhead.
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 PDFs in OCI Object Storage, then use OCI AI Document Understanding to extract text and create embeddings.
OCI Object Storage is optimized for large-scale, unstructured data like PDFs, and OCI AI Document Understanding provides a managed service to extract text from PDFs, which can then be directly fed into embedding pipelines for RAG. This eliminates the need for manual preprocessing or local compute, ensuring scalability and integration with GenAI services.
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 PDFs in OCI Object Storage, then use OCI AI Document Understanding to extract text and create embeddings.
Why this is correct
This leverages cloud-native services for scalable extraction and embedding, ideal for RAG.
- ✗
Convert PDFs to text locally, upload to OCI Database, use SQL queries to retrieve.
Why it's wrong here
Local conversion is manual and database retrieval is not optimized for vector search in RAG.
- ✗
Use OCI Data Flow to process in batch and store in NoSQL.
Why it's wrong here
Data Flow is for batch processing but adds overhead; NoSQL is not optimized for vector embeddings.
- ✗
Store PDFs in OCI File Storage, mount to compute, run offline extraction.
Why it's wrong here
Offline extraction is less efficient and lacks integration with GenAI services.
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