1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 4 — 15 Questions

Question 1

In OCI OpenSearch, a k-NN search query returns results with low precision. The index uses HNSW algorithm. The search parameters are: `k=10`, `ef_search=100`. To improve recall without significantly increasing latency, which parameter should be adjusted?

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

Accepted Answer

Increase `ef_search`. Increasing `ef_search` expands the search radius in the HNSW graph, allowing the algorithm to explore more candidate nodes during the search phase. This directly improves recall by reducing the chance of missing true nearest neighbors, while the impact on latency is typically sub-linear because HNSW's hierarchical structure limits the additional traversal cost.

Answer

Decrease `ef_search`

Answer

Decrease `k`

Answer

Increase `k`

Question 2

A troubleshooting scenario: A RAG system returns no results for certain queries. The index exists and has documents. Which TWO are likely causes?

Accepted Answer

The query embedding dimension does not match the index dimension. Vector search requires the query embedding to have the same dimensionality as the index vectors. If the query embedding dimension (e.g., 768 from a different model) does not match the index dimension (e.g., 1024), the similarity computation fails or returns no results. This is a fundamental requirement in all vector databases like FAISS, Pinecone, or pgvector.

Answer

The search algorithm is set to exact (brute-force) and index is small

Answer

The query is too long for the embedding model

Answer

The index has not been refreshed after adding new documents

Question 3

A data scientist is designing a RAG system using OCI Data Science and OCI Generative AI. Which two considerations are critical for optimal retrieval quality? (Choose 2.)

Accepted Answer

Use the same embedding model for both indexing and querying.. Using the same embedding model for both indexing and querying ensures that the vector representations of documents and queries lie in the same semantic space, which is critical for accurate cosine similarity comparisons. If different models are used, the embeddings would be misaligned, leading to poor retrieval quality even if the models are similar in architecture.

Answer

Increase the chunk size to the maximum allowed to capture more context.

Answer

Fine-tune the generation model on domain-specific data.

Answer

Use a hierarchical index structure for faster search.

Question 4

A company is deploying a RAG application for legal document analysis using OCI. Which three best practices should be followed to mitigate hallucinations? (Choose 3.)

Accepted Answer

Implement a fallback to abstain from answering if confidence is low.. Implementing a fallback to abstain from answering when confidence is low directly reduces hallucinations by preventing the model from generating speculative or incorrect responses. In RAG applications, this is often achieved by setting a confidence threshold on the retrieval scores or the generation model's output probabilities, ensuring the system only responds when it has sufficient evidence.

Answer

Include a verification step via a secondary model.

Answer

Increase the number of retrieved chunks to 10.

Question 5

A small business is building an internal Q&A bot using OCI Generative AI with RAG. They have indexed their product manuals into OCI OpenSearch using a precomputed embedding model. When they test queries, the bot often returns answers that are only partially relevant, and sometimes it cannot find answers for questions that are clearly present in the manuals. The developers suspect the chunking strategy is suboptimal. Currently, they use a fixed chunk size of 512 tokens with no overlap. What should they do to improve retrieval relevance?

Accepted Answer

Add a 20% token overlap between consecutive chunks.. Adding a 20% token overlap between chunks ensures that semantically related content spanning chunk boundaries is not lost, which directly addresses the problem of missing answers for questions present in the manuals. Overlap preserves context continuity, improving retrieval recall and relevance without sacrificing the precision that a fixed-size chunk provides.

Answer

Increase the chunk size to 1024 tokens to include more context.

Answer

Reduce the chunk size to 256 tokens to increase precision.

Answer

Switch to a sentence-based chunking strategy with no overlap.

Question 6

A company uses OCI Generative AI's chat endpoint with RAG for customer support. They have observed that the model sometimes generates answers that contradict the retrieved context. The retrieved chunks are correct and relevant, but the model ignores them. What configuration change should they implement first?

Accepted Answer

Add explicit instructions in the system prompt to base answers solely on provided context.. Adding explicit instructions in the system prompt to base answers solely on the provided context directly addresses the issue of the model ignoring retrieved context. This is the simplest and most immediate configuration change. Option A (fine-tuning) is resource-intensive and may not solve the core problem of context adherence. Option B (increasing chunk count) addresses retrieval breadth, not generation behavior. Option D (different embedding model) affects retrieval quality, but the problem states retrieved chunks are correct and relevant, so retrieval is not the issue.

Answer

Fine-tune the generation model on customer support dialogues.

Answer

Increase the number of retrieved chunks from 3 to 5.

Answer

Use a different embedding model for retrieval.

Question 7

An enterprise is deploying a RAG application for compliance document analysis using OCI. They use OCI OpenSearch as the vector store and have millions of documents. Retrieval latency is critical. Currently, a single query takes over 2 seconds. The index uses a flat (brute-force) distance computation. They have considered using approximate nearest neighbor (ANN) algorithms but are unsure about the impact on recall. They need to reduce latency to under 500ms while maintaining high recall. What should they do?

Accepted Answer

Switch to an HNSW algorithm with an appropriate M and ef_search parameters.. Switching to HNSW with appropriate parameters provides fast approximate search with configurable recall. Option A (reducing dimensions) can degrade embedding quality. Option B (reducing shards) may not achieve the required latency reduction. Option D (increasing top-k) would increase latency.

Answer

Use a smaller embedding dimension by truncating the existing embeddings.

Answer

Reduce the number of shards in the OpenSearch index to improve parallelism.

Answer

Increase the top-k parameter to retrieve more candidates then filter.

Question 8

A data scientist is using OCI Data Science to build a RAG system for medical literature. They have a large corpus of PDFs. They used the default OCI Generative AI embedding model and chunked each PDF into 512-character segments with 10% overlap. However, queries about specific drug doses often return incorrect information, even though the correct dose is present in the corpus. Upon inspection, they find that the retrieved chunks often contain partial dose information or miss the context units (e.g., mg vs. mcg). What improvement should they prioritize?

Accepted Answer

Use a semantic chunking strategy that respects document structure (e.g., paragraphs, sections).. The core issue is that fixed-size character chunking (512 characters) breaks medical documents mid-sentence or mid-section, causing critical context like units (mg vs. mcg) to be separated from the numeric dose. Semantic chunking that respects document structure (paragraphs, sections) ensures that dose information and its units remain in the same chunk, preserving the semantic integrity needed for accurate retrieval. This directly addresses the observed problem of partial dose information and missing context units.

Answer

Implement a secondary verification step using a rule-based pattern matcher.

Answer

Increase the chunk overlap to 50% to ensure more context.

Answer

Fine-tune the embedding model on medical text.

Question 9

A large organization is deploying a multi-tenant RAG application on OCI, where each tenant has its own set of documents. They use a shared OCI OpenSearch cluster with tenant_id metadata to filter documents. They observe that occasionally, queries from one tenant return results from another tenant's documents. The security team requires strict isolation. They have verified that the metadata filter is correctly applied in the search request. What is the most likely root cause?

Accepted Answer

The tenant_id field is not indexed as a keyword, causing incorrect filtering.. The tenant_id field must be indexed as a keyword (or mapped as a keyword type) to enable exact-match filtering. If it is not indexed correctly, OpenSearch may not apply the filter precisely, leading to cross-tenant results. Option A (index not refreshed) affects availability, not isolation. Option C (embedding model training) does not cause retrieval leakage. Option D (order of filter) is incorrect because the filter is applied during search, not after; the order does not cause the issue.

Answer

The OpenSearch index has not been refreshed after ingestion of new documents.

Answer

The embedding model has been trained on data from multiple tenants, causing cross-tenant leakage.

Answer

The metadata filter is being applied after the vector search instead of before.

Question 10

Refer to the exhibit. A developer creates an index mapping for a vector search application. When performing a k-NN search query, the query fails with a parsing error. What is the most likely cause?

Accepted Answer

The dimension value 768 does not match the embedding model's output dimension.. The error occurs because the dimension value in the index mapping (768) does not match the actual output dimension of the embedding model used to generate the query vector. In OpenSearch, the k-NN search requires that the dimension of the indexed vectors exactly matches the dimension of the query vector; a mismatch causes a parsing error. Option B correctly identifies this mismatch as the root cause.

Answer

The query is missing the 'knn' query clause.

Answer

The knn setting is not enabled at the index level.

Answer

The space_type should be 'l2' instead of 'cosinesimil'.

Answer

The engine should be 'nmslib' instead of 'faiss'.

Question 11

A company has implemented a RAG-based chatbot using OCI Generative AI and OCI OpenSearch as the vector store. The chatbot answers questions about internal policies. The team uses a dense vector embedding model with 768 dimensions and the HNSW algorithm. The corpus contains 5 million documents. Users report that the chatbot takes 8-12 seconds to respond, and the answers are often not relevant, missing key policy details. Upon investigation, the team finds that the k-NN search returns results based solely on vector similarity, ignoring exact keyword matches that are critical for policy documents. Which course of action will most effectively improve both response time and relevance?

Accepted Answer

Implement hybrid search using a combination of match (keyword) and k-NN (vector) queries with boosting.. Hybrid search combines keyword (match) queries with k-NN vector queries, using boosting to prioritize exact keyword matches for policy documents while still leveraging semantic similarity. This directly addresses the irrelevance issue by ensuring critical policy terms are matched exactly, and the response time improves because the hybrid query can efficiently filter the search space, reducing the number of irrelevant vector comparisons. OCI OpenSearch natively supports this hybrid approach through its query DSL, allowing you to tune the weight of each component.

Answer

Increase the number of OpenSearch data nodes to 5 and use higher-memory instances.

Answer

Reduce the ef_search parameter to 100 and retrain the embedding model on domain-specific data.

Answer

Switch to OCI Generative AI's built-in vector store instead of OpenSearch.

Question 12

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.. Adjusting chunk size and overlap helps create coherent chunks that align with query intent, improving retrieval relevance. Option A is wrong because the embedding model type (dense vs. sparse) affects retrieval method but does not directly fix chunk coherence issues. Option C is wrong because increasing chunk size may introduce noise and irrelevant context. Option D is wrong because reducing the number of retrieved chunks (k) only limits results, not improves relevance of individual chunks.

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 13

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.. 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 14

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.. Implementing a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer directly reduces hallucinations by ensuring the generated output is supported by the retrieved context. Option B is wrong because increasing temperature increases randomness and may lead to more hallucinations. Option C is wrong because increasing the number of retrieved chunks (k) can introduce irrelevant or conflicting information, potentially increasing hallucinations. Option D is wrong because using a larger generative model does not inherently improve faithfulness to the retrieved context and adds computational 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 15

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.. 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.