1Z0-1127-25 Building LLM Applications with RAG and Vector Search — 50 Questions

Question 1

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?

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

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 2

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.

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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 3

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.

Answer

The embedding model was fine-tuned on outdated data.

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

Answer

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

Question 4

An engineer configured the above index mapping for vector search. When performing a k-NN search, the results are unexpected. What is the most likely issue?

Accepted Answer

The space type at the index level and mapping level are mismatched.. OpenSearch requires the space type to be consistently defined at both the index-level settings (method.parameters.space_type) and the field-level mapping (space_type). A mismatch between these two causes the k-NN search to behave unexpectedly, as the engine uses the index-level setting for distance computation while the mapping-level setting may be used for validation or other purposes.

Answer

The space type 'cosinesimil' is not supported; it should be 'cosine'.

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The dimension 768 does not match the embedding model's output dimension.

Answer

The mapping uses 'knn_vector' type with 'faiss' engine, which is incompatible.

Question 5

A healthcare company is building a RAG-based chatbot to answer patient queries using medical documents stored in OCI Object Storage. They use OCI Generative AI service with Cohere Command R+ model and OCI OpenSearch as the vector database. The chatbot is deployed on OCI Compute with a Flask application. After deployment, the latency for each query is 15-20 seconds, which is unacceptable. Logs show that the embedding generation step (using OCI Generative AI embedding API) takes 8-10 seconds, and the vector search in OpenSearch takes 5-7 seconds. The team has already enabled connection pooling and increased the compute instance shape to the maximum allowed. Which action would MOST effectively reduce the overall latency?

Accepted Answer

Pre-generate embeddings for all documents during ingestion and store them in the vector database, so at query time only the query embedding is generated and compared.. The primary bottleneck is the embedding generation step (8-10 seconds). By pre-generating embeddings for all documents during ingestion and storing them in the vector database, the query-time embedding generation is eliminated, reducing the per-query latency to only the time needed to generate the query embedding and perform the vector search. This directly addresses the largest contributor to the 15-20 second latency.

Answer

Implement a caching layer with Redis to store previous query results and serve cached responses for identical queries.

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Reindex the OpenSearch vector index with optimal settings (e.g., HNSW algorithm, ef_search param) to speed up vector search.

Answer

Switch to a faster embedding model like Cohere Embed v3 (English) which has lower latency.

Question 6

A team uses OCI OpenSearch as a vector database for RAG. Some queries return no results despite relevant documents being indexed. What is a likely cause?

Accepted Answer

The number of candidates (k) is set too low. In OCI OpenSearch, the k parameter (number of nearest neighbors) determines how many candidate vectors are retrieved during a k-NN search. If k is set too low, the search may miss relevant documents even if they are indexed, because the algorithm only returns the top-k closest vectors. Increasing k broadens the search space and improves recall.

Answer

The vector index is not refreshed after adding documents

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The query text is too long for the embedding model

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The embedding model is incompatible with the document language

Question 7

A team fine-tunes an embedding model for a legal document RAG system but observes low retrieval recall. Which technique is most likely to improve recall?

Accepted Answer

Use hard negative mining during training. Hard negative mining exposes the model to challenging negatives during training, which sharpens the embedding space and improves recall.

Answer

Use a smaller batch size

Answer

Reduce the learning rate

Answer

Increase the number of fine-tuning epochs

Question 8

Which THREE of the following are likely causes if retrieval returns no results despite documents being indexed in an OCI OpenSearch vector store?

Accepted Answer

The k-NN algorithm is misconfigured (e.g., k=0). Option B is correct because if the k-NN search parameter k is set to 0 (or otherwise misconfigured), the query requests zero nearest neighbors and the vector store returns an empty result set even though documents are indexed. Option D is correct because an incorrect database connection string means the client never reaches the OCI OpenSearch cluster, so the query executes against nothing and returns no hits despite the index existing elsewhere. Option E is correct because if the index is not fully built or has not been refreshed, newly indexed documents are not yet searchable, so retrieval returns no results until the index becomes visible. Option A is not selected because an embedding dimension mismatch typically causes an indexing or query error (e.g., a mapping/validation failure) rather than silently returning zero results from an already indexed store. Option C is not selected because an out-of-distribution query embedding would still return some nearest neighbors (just poor-quality ones), not an empty result set.

Answer

The embedding model dimension mismatch

Answer

The query embedding is out of distribution

Question 9

Refer to the exhibit. What is a potential issue with this OCI OpenSearch index template configuration?

Accepted Answer

The space_type in settings (l2) differs from the method's space_type (cosinesimil). The index template's settings specify `space_type: l2` (Euclidean distance), while the method's `space_type` is `cosinesimil` (cosine similarity). This mismatch causes OpenSearch to use inconsistent distance metrics during indexing and search, leading to incorrect similarity calculations and degraded retrieval quality.

Answer

The ef_construction parameter is set too low

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Number of replicas is 0, which provides no redundancy

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The dimension 1024 is too large for the knn_vector type

Question 10

An organization is experiencing low recall in their RAG system. They are using OCI OpenSearch as the vector store with cosine similarity. After reviewing the retrieved chunks, they notice that relevant documents are not being returned. Which configuration change is most likely to improve recall?

Accepted Answer

Reduce the chunk size to capture more granular information.. Low recall means the system is missing relevant documents. Reducing chunk size increases the number of chunks, which can capture more granular information and improve the chance that a relevant passage is retrieved. In OCI OpenSearch with cosine similarity, smaller chunks often lead to higher recall because the vector representation can more precisely match query semantics.

Answer

Use a deterministic ID generator for consistent chunk IDs.

Answer

Increase the chunk size to provide more context per chunk.

Answer

Switch similarity metric from cosine to Euclidean distance.

Question 11

A healthcare company is deploying a RAG application using OCI Generative AI and wants to ensure patient data privacy. They cannot send sensitive data to a public embedding endpoint. Which approach should they take to embed documents while maintaining data residency and security?

Accepted Answer

Provision a dedicated AI cluster in their OCI tenancy to host the embedding model.. Provisioning a dedicated AI cluster in your OCI tenancy ensures that the embedding model runs entirely within your own cloud boundary, so patient data never leaves your tenancy. This approach satisfies strict data residency and security requirements by keeping sensitive data on-premises in the cloud, avoiding any public endpoint exposure. It also allows you to control encryption, network isolation, and compliance with regulations like HIPAA.

Answer

Use the standard OCI Generative AI public endpoint with data encryption in transit.

Answer

Use an external embedding service that complies with HIPAA in a different cloud region.

Answer

Hash the document text before sending to the public embedding endpoint.

Question 12

A developer is building a RAG pipeline using OCI Data Science and wants to store vector embeddings. Which OCI service is optimized for vector search and can be used as a vector store?

Accepted Answer

OCI OpenSearch. B is correct because OCI OpenSearch is a fully managed, search and analytics engine that natively supports k-nearest neighbor (k-NN) search on dense vector embeddings. It provides optimized indexing and querying for high-dimensional vectors, making it the ideal vector store for a RAG pipeline in OCI Data Science.

Answer

OCI Autonomous Database

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OCI Object Storage

Answer

OCI Streaming

Question 13

A company is using Oracle Database 23ai AI Vector Search for their RAG pipeline. They notice that similarity search often returns chunks that are semantically unrelated but syntactically similar due to token overlap. Which vector index type should they consider to improve semantic relevance?

Accepted Answer

HNSW index. HNSW (Hierarchical Navigable Small World) indexes are designed to prioritize semantic similarity over exact token overlap by constructing a multi-layer graph that navigates based on vector distance in the embedding space. Unlike IVF-based indexes, which rely on coarse quantization and can be misled by high-frequency token patterns, HNSW's graph-based search inherently captures the global structure of the embedding space, making it more robust to syntactically similar but semantically unrelated chunks.

Answer

IVF_SQ8 index

Answer

IVF_FLAT index

Answer

Use the default index type, which is IVF_FLAT

Question 14

Which THREE factors should be considered when choosing a vector store for a RAG application in OCI?

Accepted Answer

Capability of hybrid search (vector + keyword).. Option B is correct because hybrid search, which combines dense vector similarity with keyword/lexical matching, directly affects retrieval quality in RAG pipelines by improving recall for exact terms, acronyms, and rare tokens that pure embeddings may miss. Option D is correct because multi-tenancy and isolation determine whether the vector store can securely partition data across tenants, namespaces, or collections, which is essential for access control and compliance in shared OCI deployments. Option E is correct because indexing latency for adding new vectors governs how quickly freshly ingested or updated documents become searchable, directly impacting RAG freshness and ingestion throughput. Option A is not among the marked correct answers, and option C is not correct because the CPU architecture of the vector store nodes is an infrastructure detail abstracted away by the managed service and does not drive vector-store selection for RAG.

Answer

The maximum vector dimension supported.

Answer

The CPU architecture of the vector store nodes.

Question 15

A DBA has created the above vector index. After running queries, they observe that recall is lower than expected for approximate searches. Which change would most likely improve recall while maintaining query performance?

Accepted Answer

Increase the TARGET ACCURACY value to 99.. Increasing TARGET ACCURACY to 99 directly raises the recall threshold for approximate search, forcing the vector index to retrieve more candidates during the search phase. This improves recall without changing the index structure or query parallelism, so query performance (latency) is only minimally impacted compared to switching index types or drastically altering neighbor partitions.

Answer

Change the index type from IVF to HNSW.

Answer

Increase the number of neighbor partitions (NEIGHBOR PARTITIONS) to 8.

Answer

Reduce the number of neighbor partitions to 2.

Question 16

A company is building a RAG application using OCI Generative AI and OCI Search with OpenSearch. Users report that the responses from the LLM are not relevant to the queries, even though the document chunks seem appropriate. What is the most likely cause?

Accepted Answer

Reranking is not enabled in the OpenSearch query.. In a RAG pipeline, retrieving the top-K chunks by vector similarity often returns chunks that are semantically close but not the most relevant to the specific query. Reranking applies a cross-encoder or relevance model to rescore the retrieved candidates, reordering them so the most relevant chunks are passed to the LLM. If reranking is not enabled, the LLM receives poorly ordered context, producing irrelevant answers even when the chunks themselves are appropriate.

Answer

The embedding model is not suited for the domain.

Answer

The top K value is set too high.

Answer

The chunk size is too small, causing loss of context.

Question 17

An application using OCI Generative AI returns a 403 Forbidden error when attempting to invoke a model. The user's API key is valid and the endpoint is correct. What is the most likely cause?

Accepted Answer

The IAM policy does not grant the necessary permission to use the model.. A 403 Forbidden error indicates that the server understood the request but refused to authorize it. Since the API key is valid and the endpoint is correct, the most likely cause is that the IAM policy attached to the user or resource does not include a statement granting the `inference` permission for the specific model family or compartment. OCI Generative AI requires explicit IAM policies to allow model invocation, and a missing or misconfigured policy will result in a 403 even with a valid key.

Answer

The model is out of capacity.

Answer

The region is not supported for the selected model.

Answer

The API request body is malformed.

Question 18

Which OCI service provides a managed vector database capability that can be used as a knowledge base in a RAG architecture?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch provides a managed vector database capability through its k-nearest neighbor (k-NN) plugin, which supports storing and querying vector embeddings. This makes it suitable as a knowledge base in a Retrieval-Augmented Generation (RAG) architecture, where vector similarity search retrieves relevant context for LLM prompts.

Answer

OCI MySQL HeatWave

Answer

OCI Database (Autonomous Database)

Answer

OCI Object Storage

Question 19

Which TWO are required components to implement a basic RAG system using OCI services? (Choose two.)

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch (D) is correct because a RAG system needs a vector store to index and retrieve document embeddings; OCI Search with OpenSearch supports vector search and serves as the retrieval layer that supplies relevant context to the LLM. OCI Document Understanding (E) is correct because it extracts text and structured content from source documents (PDFs, images, forms), which is a prerequisite for chunking and embedding in a basic RAG pipeline. OCI Object Storage (A) is not required for a basic RAG implementation, since documents can be ingested directly and the vector index lives in OpenSearch. OCI Functions (B) is not a required component; it is an optional serverless compute service for orchestration or custom logic. OCI Data Flow (C) is not required either, as it is a managed Spark service for large-scale batch processing, which is unnecessary for a basic RAG setup.

Answer

OCI Object Storage

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OCI Functions

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OCI Data Flow

Question 20

A company has deployed a RAG application using OCI Generative AI service with a vector store in OCI OpenSearch. Users report that answers are often incomplete or irrelevant. The application uses a single prompt template with a fixed chunk size of 1000 tokens. Which action is most likely to improve answer quality?

Accepted Answer

Implement a re-ranking step after vector search. Implementing a re-ranking step after vector search improves answer quality by reordering the top-K retrieved chunks based on their relevance to the query, using a cross-encoder or similar model. This addresses the issue of incomplete or irrelevant answers caused by the fixed chunk size and single prompt template, as re-ranking ensures that only the most contextually appropriate chunks are passed to the LLM for generation.

Answer

Disable vector search and rely solely on the LLM's pre-trained knowledge

Answer

Use a smaller embedding model to reduce noise

Answer

Increase the chunk size to 2000 tokens