1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A healthcare startup is building a chatbot that retrieves patient treatment guidelines using OCI Generative AI Service and OCI OpenSearch. They require that all retrieved documents are from approved sources only and that the system can explain which source was used for each response. Which combination of features should they implement?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model.
Option A is correct because it directly addresses both requirements: a metadata filter on `source_type='approved'` ensures only approved documents are retrieved from OpenSearch, and including document IDs in the context allows the model to cite the specific source for each response. This approach enforces access control at the retrieval layer while providing traceability, which is essential for compliance in healthcare applications.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model.
Why this is correct
Metadata filtering enforces source restriction; document IDs provide provenance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Rely on the vector search's cosine similarity to rank approved sources higher.
Why it's wrong here
Similarity ranking does not guarantee exclusion of non-approved sources.
✗
Use prompt engineering to ask the model to ignore non-approved sources.
Why it's wrong here
Prompting is not a reliable enforcement mechanism.
✗
Reduce the top-K value to limit the number of retrieved documents.
Why it's wrong here
Top-K limits count, not source.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume semantic similarity or prompt engineering alone can enforce access control, but in RAG systems, retrieval-layer filtering is the only reliable way to restrict document access before the model sees the content.
Trap categories for this question
Similar concept trap
Similarity ranking does not guarantee exclusion of non-approved sources.
Detailed technical explanation
How to think about this question
In OCI OpenSearch, metadata filtering is implemented via a Boolean query that combines a vector search with a term filter on a document field (e.g., `source_type`). This ensures that only documents matching the filter are considered during the k-NN search, effectively creating a secure retrieval pipeline. Including document IDs in the context allows the LLM to reference the exact source, which can be used for audit trails or to satisfy regulatory requirements like HIPAA's right to explanation.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model. — Option A is correct because it directly addresses both requirements: a metadata filter on `source_type='approved'` ensures only approved documents are retrieved from OpenSearch, and including document IDs in the context allows the model to cite the specific source for each response. This approach enforces access control at the retrieval layer while providing traceability, which is essential for compliance in healthcare applications.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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