1Z0-1127-25 OCI Generative AI Service Practice Question
A company is building a document summarization pipeline using OCI Generative AI. They need to summarize thousands of legal documents efficiently. Which approach minimizes cost while maintaining quality?
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
✓
Use the OCI Generative AI Summarisation API with a prebuilt model
The Summarisation API is designed for efficient, cost-effective summarization. Dedicated clusters are expensive for batch workloads, and embedding-based approaches add complexity and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the OCI Generative AI Summarisation API with a prebuilt model
Why this is correct
The Summarisation API is purpose-built for summarization, uses on-demand pricing, and is cost-efficient for large volumes.
- ✗
Fine-tune a model on legal documents and deploy on a dedicated cluster
Why it's wrong here
Fine-tuning and dedicated clusters are costly; overkill for a standard summarization task.
- ✗
Use the Chat API with a system prompt instructing summarization
Why it's wrong here
The Chat API can summarize but is less optimized and may be more expensive per token than the dedicated Summarisation API.
- ✗
Use the Embedding API to vectorize documents and then use a custom summarization algorithm
Why it's wrong here
The Embedding API generates vector representations for semantic similarity search, not for generating summaries. This pipeline requires a text generation model to produce condensed legal content, which the Embedding API cannot do. It is tempting because embeddings enable efficient document retrieval at scale, and would be correct for a search or clustering task, not for summarisation.
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