1Z0-1127 Building LLM Applications with RAG and Vector Search — Timed 15 Questions

Question 1

A developer is building a RAG application using Oracle Cloud Infrastructure (OCI) Document Understanding and OCI Generative AI. After chunking documents and generating embeddings, the developer observes that the retrieval step often returns chunks that are semantically unrelated to the query. Which action is MOST likely to improve retrieval relevance?

Accepted Answer

Adjust the chunk size and chunk overlap to better capture coherent passages.. Option C is correct because adjusting the chunk size and overlap can significantly impact the quality of retrieved passages. Option A is wrong because increasing the chunk size may introduce more noise. Option B is wrong because reducing the number of retrieved chunks could miss relevant information. Option D is wrong because the embedding model is already chosen; changing it may not fix the chunking issue.

Answer

Switch from a dense embedding model to a sparse embedding model.

Answer

Increase the chunk size to capture more context.

Answer

Reduce the number of retrieved chunks (k) in the vector search.

Question 2

An organization stores its knowledge base in Oracle Autonomous Database and wants to build a RAG chatbot using OCI Generative AI. The chatbot must retrieve the most relevant documents based on user queries. Which indexing approach is BEST suited for efficient similarity search on text embeddings?

Accepted Answer

Create an ANN index on the embedding vector column.. Option A is correct because Approximate Nearest Neighbor (ANN) indexes are specifically designed for high-dimensional vector spaces, enabling efficient similarity search on embedding vectors. In Oracle Autonomous Database, ANN indexes (e.g., using IVF or HNSW algorithms) drastically reduce search latency compared to brute-force scans, which is critical for real-time RAG chatbot responses.

Answer

Create a bitmap index on the embedding vector column.

Answer

Create an inverted index on the document text column.

Answer

Create a B-tree index on the document text column.

Question 3

A company is deploying a RAG pipeline using OCI Data Science and OCI Generative AI. The pipeline uses a Cohere command model for generation and a Cohere embed model for retrieval. The team notices that the model occasionally produces hallucinated answers that are not supported by the retrieved context. Which strategy is MOST effective at reducing hallucinations?

Accepted Answer

Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer.. Option D is correct because incorporating a faithfulness check that re-ranks retrieval results can directly filter out unsupported claims. Option A is wrong because increasing temperature may increase randomness and hallucinations. Option B is wrong because more retrieved chunks can introduce conflicting information. Option C is wrong because a larger model does not guarantee faithfulness and increases cost.

Answer

Increase the temperature parameter of the generation model.

Answer

Increase the number of retrieved chunks (k) to provide more context.

Answer

Use a larger generative model with more parameters.

Question 4

A data scientist is building a RAG application that processes PDF invoices. The extraction step uses OCI Document Understanding to convert PDFs to text. The scientist then splits the text into chunks and generates embeddings using OCI Generative AI. However, the retrieval often misses critical fields like invoice numbers and dates. Which preprocessing step would MOST likely improve retrieval of these specific fields?

Accepted Answer

Tag each chunk with metadata such as invoice number, date, and vendor, and use metadata filtering during retrieval.. Option C is correct because metadata tagging and filtering directly address the retrieval of specific fields like invoice numbers and dates. By attaching metadata (e.g., invoice number, date, vendor) to each chunk and filtering on these metadata fields during retrieval, the RAG system can precisely locate the relevant chunks without relying solely on semantic similarity. This approach leverages OCI Document Understanding's ability to extract structured data and OCI Generative AI's vector search capabilities to combine dense embeddings with exact metadata matching.

Answer

Increase the chunk size to include entire invoices.

Answer

Apply stemming and lemmatization to the text before chunking.

Answer

Switch from dense embeddings to sparse embeddings for better exact match.

Question 5

A developer is using OCI Generative AI to build a question-answering system over a large corpus of technical manuals. The developer uses the Cohere Embed model to generate embeddings and stores them in an OCI OpenSearch cluster. Queries are slow and the team needs to reduce latency. Which approach is BEST for improving search speed while maintaining acceptable accuracy?

Accepted Answer

Reduce the k value in the nearest neighbor search.. Reducing the k value in the nearest neighbor search directly decreases the number of vectors that must be compared during query time, which lowers latency. In approximate nearest neighbor (ANN) search, a smaller k means fewer candidates are evaluated, speeding up retrieval while still maintaining acceptable accuracy if the original k was unnecessarily high. This is the most effective tuning knob for latency in vector search systems like OCI OpenSearch with Cohere embeddings.

Answer

Increase the embedding dimension for better representation.

Answer

Use exact nearest neighbor search instead of approximate.

Answer

Increase the index refresh interval to reduce write overhead.

Question 6

A team is deploying a RAG system that uses OCI Generative AI to answer questions about internal HR policies. The system must comply with data residency requirements: all data processing must stay within a specific OCI region. The team uses OCI Data Science for orchestration. Which architecture BEST meets the data residency requirement?

Accepted Answer

Deploy the generative AI model endpoints within the same OCI region as the data and compute.. Option A is correct because deploying the generative AI model endpoints within the same OCI region as the data and compute ensures that all data processing—including inference, embedding generation, and vector search—occurs entirely within the required region, satisfying data residency requirements. OCI Generative AI endpoints are region-specific and do not automatically route requests to other regions, so co-locating all components avoids any cross-region data transfer.

Answer

Use OCI Generative AI endpoints in a different region but store data in the required region.

Answer

Use an external third-party LLM endpoint that guarantees data residency.

Answer

Store embeddings in a different region but run inference in the required region.

Question 7

A developer notices that the RAG system returns irrelevant chunks when the user query contains typos or abbreviations. Which technique would BEST improve retrieval robustness for such queries?

Accepted Answer

Implement query rewriting or expansion using a language model before embedding.. Option D is correct because query rewriting or expansion using a language model (LLM) directly addresses typos and abbreviations by generating a corrected or enriched query before embedding. This improves the semantic alignment between the user's intent and the vector search, ensuring that even noisy input retrieves relevant chunks. Techniques like spelling correction or synonym expansion at query time are far more effective than post-retrieval fixes or parameter tuning.

Answer

Decrease the chunk size to focus on smaller units.

Answer

Increase the number of retrieved chunks to cover more variations.

Answer

Use a spell-checker on the retrieved chunks.

Question 8

Which TWO are best practices for building a RAG application on OCI? (Choose two.)

Accepted Answer

Use a vector database such as OCI OpenSearch with ANN indexes for storing embeddings.. Option A is correct because OCI OpenSearch with Approximate Nearest Neighbor (ANN) indexes is a best practice for vector storage and retrieval in RAG applications. ANN indexes enable efficient similarity search over high-dimensional embeddings, which is essential for retrieving relevant context from large document collections at low latency.

Answer

Generate embeddings for documents at query time to ensure freshness.

Answer

Store the source documents only in OCI Object Storage and retrieve them at query time using full-text search.

Answer

Use a different embedding model for documents and queries to capture distinct semantics.

Question 9

Which THREE are valid considerations when designing a RAG pipeline that uses OCI Generative AI and OCI OpenSearch? (Choose three.)

Accepted Answer

The quality of the text extraction from OCI Document Understanding directly impacts retrieval accuracy.. Option C is correct because OCI Document Understanding performs text extraction from documents (e.g., PDFs, images). If the extraction is poor (e.g., missing text, OCR errors), the resulting chunks will be inaccurate, directly degrading the quality of vector embeddings and thus retrieval accuracy in the RAG pipeline.

Answer

OCI OpenSearch only supports Euclidean distance for vector similarity.

Answer

Each document must be converted to a single vector for efficient retrieval.

Question 10

Which TWO are common causes of poor answer quality in a RAG system built on OCI Generative AI? (Choose two.)

Accepted Answer

Mismatch between the embedding model's training data and the domain of the documents.. Option A is correct because the embedding model's training data determines the semantic space in which documents and queries are represented. If the model was trained on general text (e.g., Wikipedia) but the documents are from a specialized domain (e.g., medical or legal), the embeddings will fail to capture domain-specific nuances, leading to poor retrieval relevance and thus poor answer quality in the RAG system.

Answer

Using a generation model that is too large for the task.

Answer

Setting the temperature parameter too low, causing overly deterministic outputs.

Answer

Using only vector search without keyword-based fallback.

Question 11

A manufacturing company uses OCI OpenSearch to build a RAG application that retrieves procedural documents. After deployment, queries often return outdated procedures even though the vector index was refreshed. What is the most likely cause?

Accepted Answer

The full-text search index is not synchronized with the vector index after updates.. Option B is correct because in a RAG application using OCI OpenSearch, the vector index and full-text search index are separate. When procedural documents are updated, the full-text search index may reflect changes immediately, but the vector index requires explicit re-indexing or synchronization to update embeddings. If the vector index is not refreshed after updates, queries can still retrieve outdated vector representations, leading to outdated results despite the index being refreshed.

Answer

The embedding model was fine-tuned on outdated data.

Answer

The BM25 scoring algorithm prioritizes older documents due to term frequency.

Answer

The chunk overlap percentage is too high, causing duplicate context.

Question 12

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?

Accepted Answer

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.

Answer

Rely on the vector search's cosine similarity to rank approved sources higher.

Answer

Use prompt engineering to ask the model to ignore non-approved sources.

Answer

Reduce the top-K value to limit the number of retrieved documents.

Question 13

A company uses a RAG pipeline with OCI Data Science and Cohere embeddings. They notice that retrieval recall is low for domain-specific acronyms. What is the best practice to improve this?

Accepted Answer

Expand acronyms to their full forms during document preprocessing and indexing.. Expanding acronyms to their full forms during document preprocessing and indexing ensures that the embedding model can map the acronym to its semantic meaning, improving retrieval recall for domain-specific terms. Cohere embeddings are trained on general text, so without expansion, acronyms like 'NLP' may not match queries for 'Natural Language Processing' in vector space. This preprocessing step directly addresses the root cause of low recall for acronyms.

Answer

Reduce the cosine similarity threshold in the vector search.

Answer

Fine-tune the embedding model with domain-specific acronyms.

Answer

Increase the chunk size to include more context around acronyms.

Question 14

A financial firm deploys a RAG application using OCI OpenSearch. They observe that the LLM sometimes generates incorrect answers that are not supported by the retrieved documents. Which technique directly addresses this issue?

Accepted Answer

Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.. Option C is correct because it directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.

Answer

Use a more detailed system prompt instructing the model to not make up information.

Answer

Increase the temperature parameter of the LLM to reduce creativity.

Answer

Increase the number of retrieved documents to provide more context.

Question 15

A research institution uses OCI Data Flow to process large-scale document corpora for a RAG system. They want to minimize latency for end-user queries. Which architecture decision would most effectively reduce query latency?

Accepted Answer

Precompute embeddings offline using OCI Data Flow and store them in an OCI OpenSearch index.. Precomputing embeddings offline with OCI Data Flow and storing them in an OCI OpenSearch index eliminates the need to generate embeddings at query time, which is the primary source of latency. This approach shifts the computationally expensive embedding generation to a batch process, allowing queries to perform only a fast vector similarity search against the precomputed index, drastically reducing end-user response time.

Answer

Embed documents on-the-fly during query time to ensure freshness.

Answer

Use a larger, more accurate embedding model.

Answer

Increase the number of Spark workers for parallel processing of queries.