1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 5 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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.

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Increase the embedding dimension for better representation.

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Use exact nearest neighbor search instead of approximate.

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Increase the index refresh interval to reduce write overhead.

Question 2

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.. 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.

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

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

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

Question 3

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.. 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.

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Decrease the chunk size to focus on smaller units.

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Increase the number of retrieved chunks to cover more variations.

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Use a spell-checker on the retrieved chunks.

Question 4

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.. 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.

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Generate embeddings for documents at query time to ensure freshness.

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

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

Question 5

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.. 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.

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OCI OpenSearch only supports Euclidean distance for vector similarity.

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

Question 6

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.. 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.

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

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

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Using only vector search without keyword-based fallback.

Question 7

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.. 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.

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The embedding model was fine-tuned on outdated data.

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

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

Question 8

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.. 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.

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

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

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

Question 9

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.

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Reduce the cosine similarity threshold in the vector search.

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

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Increase the chunk size to include more context around acronyms.

Question 10

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.. 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.

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

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Increase the temperature parameter of the LLM to reduce creativity.

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Increase the number of retrieved documents to provide more context.

Question 11

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.

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

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Use a larger, more accurate embedding model.

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

Question 12

A retail company uses OCI Generative AI Service to build a RAG chatbot for product recommendations. The chatbot should consider both the user's query and the retrieved product descriptions. Which component of the RAG pipeline is responsible for combining these inputs before sending to the LLM?

Accepted Answer

Prompt template. The prompt template is the component in a RAG pipeline that structures the final input to the LLM by combining the user's query with the retrieved product descriptions. It defines the format and instructions (e.g., 'Based on these product descriptions, recommend...') that the LLM uses to generate a coherent response. Without a prompt template, the raw query and documents would be sent without context, leading to poor or irrelevant outputs.

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Reranker

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Document retriever

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Embedding model

Question 13

Which TWO actions are best practices when deploying a RAG application using OCI OpenSearch and OCI Generative AI?

Accepted Answer

Implement a reranker to improve the relevance of retrieved documents.. Implementing a reranker improves retrieval precision by re-scoring the top-k documents from the initial vector search using a cross-encoder model, which captures deeper semantic relevance than cosine similarity alone. In OCI OpenSearch, this is typically done via a post-processing step with OCI Generative AI or a dedicated reranking model, ensuring only the most contextually relevant chunks are passed to the LLM for generation.

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Embed every document chunk in real-time during query processing.

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Use very small chunk sizes (e.g., 50 tokens) to maximize granularity.

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Set the LLM temperature to 1.5 to encourage diverse outputs.

Question 14

Which THREE factors should be considered when designing a vector search index for a RAG application that supports multiple languages?

Accepted Answer

Implement language identification as a preprocessing step.. Language identification as a preprocessing step ensures that documents are correctly tagged before indexing, which allows the system to apply appropriate language-specific tokenization, stop-word removal, and stemming. This prevents cross-language contamination in the vector index and improves retrieval accuracy for a multilingual RAG application.

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Create separate vector indexes for each language.

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Use larger chunk sizes for languages with complex morphology.

Question 15

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

Accepted Answer

The API rate limit has been exceeded; wait for the retry period and implement exponential backoff.. The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

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The model is deprecated; update to the latest model.

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The endpoint URL is incorrect; verify the OCI region endpoint.

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The request payload is malformed; check the input format.