1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 2 — 15 Questions

Question 1

Which TWO of the following are best practices when indexing documents for a RAG application using OCI OpenSearch?

Accepted Answer

Enable chunk overlap to avoid splitting important information across chunks.. Chunk overlap ensures that semantically related content that spans chunk boundaries is preserved, preventing the loss of important context when generating embeddings. In OCI OpenSearch, overlapping chunks help maintain continuity for downstream retrieval, as the retriever can still access the full context even if a sentence or concept is split across two chunks.

Answer

Use the same chunk size for all documents regardless of content type.

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Apply stemming to reduce vocabulary size.

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Remove all stop words from the text before embedding.

Question 2

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).. Hybrid search combines vector similarity search with keyword-based (BM25) retrieval, which significantly improves relevance for RAG applications by handling exact term matches and semantic similarity together. OCI's OpenSearch and AI Vector Search support hybrid search, enabling more accurate context retrieval for LLM prompts.

Answer

The maximum vector dimension supported.

Answer

The CPU architecture of the vector store nodes.

Question 3

A developer sends the above request to the OCI Generative AI API. The response returns an error: 'InvalidParameter: The parameter 'topP' is not supported for this model.' What is the most likely reason?

Accepted Answer

The 'cohere.command-r-plus-v1:0' model does not accept the topP parameter.. The error 'InvalidParameter: The parameter 'topP' is not supported for this model' indicates that the model specified in the request, 'cohere.command-r-plus-v1:0', does not accept the 'topP' parameter. This is because the Cohere Command-R+ model uses a different sampling strategy (e.g., temperature and top-k) and does not expose a 'topP' parameter in the OCI Generative AI API. The API validates parameters against the model's capabilities, and unsupported parameters trigger this specific error.

Answer

The model ID is deprecated.

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The topP parameter value is out of range.

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The JSON request format is incorrect.

Question 4

A developer implements a RAG chatbot using OCI Generative AI with streaming enabled. The chatbot fails to remember earlier conversation turns during a session. What is the most likely cause?

Accepted Answer

The application does not include previous messages in the request.. A RAG chatbot with streaming enabled still requires the application to manage conversation state by including previous messages in each request. The OCI Generative AI streaming endpoint processes each request independently; without explicitly passing the conversation history, the model has no context of prior turns, causing it to fail to remember earlier interactions.

Answer

The max_tokens parameter is set too low.

Answer

The streaming endpoint does not support conversation history.

Answer

The temperature parameter is too high.

Question 5

A data scientist is designing a RAG system with a large vector database (hundreds of millions of documents) and requires high recall accuracy. Which vector search index type should be used in OCI Search with OpenSearch?

Accepted Answer

HNSW (Hierarchical Navigable Small World). HNSW is the correct choice because it provides the best trade-off between high recall and low latency for large-scale vector search. It builds a multi-layer graph structure that enables efficient approximate nearest neighbor search, achieving recall accuracy close to brute-force while scaling to hundreds of millions of documents.

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LSH (Locality Sensitive Hashing)

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Flat (brute-force)

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IVF (Inverted File Index)

Question 6

What is a recommended practice to prevent the LLM from generating information not present in the retrieved context when building a RAG application?

Accepted Answer

Using a system message that says 'Use only the provided context to answer.'. Explicitly instructing the LLM via a system message to 'Use only the provided context to answer' directly constrains the model to rely solely on the retrieved documents, reducing the risk of hallucination. This prompt engineering technique leverages the model's instruction-following capability to suppress parametric knowledge and enforce grounded generation.

Answer

Setting the temperature to 0.

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Including few-shot examples in the prompt.

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Increasing the topK value.

Question 7

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.

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The API request body is malformed.

Question 8

Which TWO are best practices for chunking documents in a RAG pipeline? (Choose two.)

Accepted Answer

Use semantic chunking to preserve meaning.. Semantic chunking (D) preserves the natural boundaries of the text, such as paragraphs or sentences, ensuring that each chunk retains coherent meaning. This is critical in RAG pipelines because it improves retrieval accuracy by preventing the splitting of related concepts, which would otherwise degrade the quality of the context passed to the LLM.

Answer

Use fixed-size chunks regardless of content boundaries.

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Always use small chunks (e.g., 100 characters).

Answer

Chunk entire documents as a single chunk.

Question 9

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

Question 10

A team has set up a RAG pipeline using OCI Data Science with OCI OpenSearch as the vector store. The embedding model is from the OCI Generative AI service. Users note that the vector search returns irrelevant documents for many queries. Which of the following is the most likely cause?

Accepted Answer

The query is not being converted to an embedding before search. In a RAG pipeline, the user's query must be converted into an embedding vector using the same embedding model that was used to embed the documents. If the query is sent as raw text to OpenSearch, it will perform a lexical (keyword) search instead of a vector similarity search, returning irrelevant results. OCI Data Science and OpenSearch require explicit embedding of the query before invoking the k-NN search endpoint.

Answer

The chunk size is too large, causing overlapping context

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The OpenSearch cluster is too small to handle the load

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The embedding model dimension does not match OpenSearch index dimension

Question 11

When building a RAG application for document retrieval, which chunking strategy is recommended to maximize retrieval accuracy?

Accepted Answer

Use overlapping chunks with a sliding window. Overlapping chunks with a sliding window ensure that context is preserved across chunk boundaries, which is critical for retrieval accuracy in RAG applications. When a query spans the boundary between two fixed-size chunks, the overlap captures the relevant context in both chunks, reducing the risk of missing key information. This strategy directly addresses the limitation of fixed-size token chunks that may split semantically related content.

Answer

Use fixed-size token chunks with no overlap

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Use random splitting points

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Use entire documents as single chunks

Question 12

A real-time customer support chatbot uses RAG with OCI Generative AI. The average response time is 5 seconds, which is too slow. The team identifies the vector search as the bottleneck. Which optimization would most reduce latency?

Accepted Answer

Use approximate nearest neighbor (ANN) search with a lower recall setting. The vector search bottleneck causing high latency is best addressed by reducing the computational cost of the nearest neighbor search. Approximate Nearest Neighbor (ANN) search with a lower recall setting trades a small amount of accuracy for a significant reduction in search time, directly targeting the root cause of the 5-second response time. This optimization is specifically designed for latency-sensitive applications like real-time chatbots.

Answer

Increase the number of retrieved documents to 10

Answer

Move the vector store to a different region

Answer

Switch to a larger embedding model for better accuracy

Question 13

A company wants to ensure their RAG application complies with data residency requirements. Data must not leave a specific OCI region. Which TWO actions are necessary? (Choose two.)

Accepted Answer

Deploy the OCI Generative AI service endpoint in the required region. Deploying the OCI Generative AI service endpoint in the required region ensures that all inference requests and data processing occur within that region, preventing data from leaving the specified boundary. This is a fundamental requirement for data residency compliance because the endpoint determines where the model processes input and generates output.

Answer

Enable cross-region access for the vector store

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Use a global search interface

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Configure the vector store to replicate across regions for high availability

Question 14

Refer to the exhibit. A RAG application logs this error when trying to search. What is the most likely cause?

Accepted Answer

The index name is misspelled in the application configuration. The error log indicates that the RAG application cannot find the specified index when performing a vector search. This is most commonly caused by a mismatch between the index name configured in the application and the actual index name in OpenSearch. Option C is correct because a misspelled index name will cause the search request to fail with a '404 index_not_found_exception' or similar error, even if the cluster is healthy and the embedding model is valid.

Answer

The embedding model is incompatible

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The OpenSearch cluster is not accessible

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The query syntax is incorrect

Question 15

A company is building a RAG application on OCI and needs a managed vector database with native support for AI Vector Search, which offers high performance and integration with OCI GenAI. Which OCI service should they use?

Accepted Answer

Autonomous Database with AI Vector Search. Autonomous Database with AI Vector Search is the correct choice because it is a fully managed OCI service that natively supports vector similarity search, enabling high-performance RAG workflows. It integrates directly with OCI GenAI for embedding generation and LLM orchestration, eliminating the need for separate vector database infrastructure.

Answer

OCI OpenSearch

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MySQL HeatWave

Answer

OCI Object Storage