1Z0-1127-25 Building LLM Applications with RAG and Vector Search — Timed 10 Questions

Question 1

A developer is building a RAG chatbot for an internal knowledge base. To ensure the system retrieves the most relevant chunks, what is the best practice for chunking?

Accepted Answer

Use semantic chunking with overlap. Semantic chunking with overlap ensures that each chunk preserves coherent meaning by splitting text at natural boundaries (e.g., sentences or paragraphs) rather than arbitrary token counts. The overlap allows the retriever to capture context that spans chunk boundaries, which is critical for RAG systems to retrieve the most relevant chunks for the LLM to generate accurate answers.

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Use very small chunks

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Use fixed-size chunks without overlap

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Use random-sized chunks

Question 2

A company uses OCI Generative AI to create embeddings for a vector search. They notice high latency in search queries. What is one possible optimization?

Accepted Answer

Use approximate nearest neighbor (ANN) search. To reduce latency in vector search, using approximate nearest neighbor (ANN) search is the standard optimization. ANN algorithms, such as those available in OCI Vector Search (e.g., HNSW, IVF), trade a small amount of accuracy for a significant reduction in query latency compared to exact search. Option A (decrease batch size) affects embedding creation, not search latency. Option C (exact search) typically increases latency due to exhaustive distance computations. Option D (increase embedding dimension) increases computational overhead and latency.

Answer

Decrease batch size for embedding creation

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Use exact search for better accuracy

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Increase the embedding dimension

Question 3

An application uses RAG to answer customer queries, but answers are often incomplete because the retrieved chunks do not contain full context. Which adjustment should the developer make?

Accepted Answer

Increase the number of retrieved chunks. Increasing the number of retrieved chunks (C) directly addresses incomplete answers by providing more context from the vector database. With RAG, the retriever fetches the top-k chunks most similar to the query; raising k increases the breadth of information passed to the LLM, reducing the chance that critical context is missing. This is the most straightforward fix when chunks themselves are correctly sized but the retrieved set lacks full coverage.

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Use a different embedding model

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Increase chunk overlap

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Decrease chunk size

Question 4

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.

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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 5

A developer uses OCI Generative AI with a custom OCI OpenSearch vector store. The text generation model sometimes hallucinates facts not in the retrieved documents. What is the most effective mitigation?

Accepted Answer

Use prompt engineering to instruct the model to stick to the provided context. Prompt engineering that instructs the model to strictly adhere to the provided context is the most effective mitigation because it directly addresses the root cause of hallucination: the model's tendency to generate information beyond the retrieved documents. By explicitly constraining the model's behavior through the system or user prompt, you reduce the likelihood of fabricated facts without altering the retrieval or generation parameters.

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Use a larger retrieval chunk size

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Increase the temperature

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Decrease the maximum token length

Question 6

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). Setting k=0 in a k-NN search means requesting zero nearest neighbors, which will always return an empty result set regardless of indexed documents. OCI OpenSearch uses the k-NN plugin where k must be a positive integer (typically ≥1) to retrieve any results; a misconfiguration like k=0 is a common operational error that causes no matches to be returned.

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The embedding model dimension mismatch

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The query embedding is out of distribution

Question 7

Refer to the exhibit. Why did the embedding creation fail?

Accepted Answer

The API call was not properly authenticated. The 401 status code in the exhibit indicates an authentication failure. The API call requires a valid OAuth 2.0 token or API key in the Authorization header; without proper authentication, the embedding creation request is rejected regardless of input length, model availability, or region enablement.

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The input text is too short

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The model ID is not available in the us-ashburn-1 region

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The region is not enabled for the Generative AI service

Question 8

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.

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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 9

Refer to the exhibit. What is the best action to resolve this error?

Accepted Answer

Reduce the number of retrieved documents. The error is caused by exceeding the context window limit of the LLM when too many retrieved documents are passed to the model. Reducing the number of retrieved documents ensures the total token count stays within the model's maximum context length, preventing truncation or failure. This directly addresses the root cause without sacrificing generation quality or altering chunking strategy.

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Decrease the temperature of the generation model

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Increase the max_tokens parameter for generation

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Use a smaller chunk size for documents

Question 10

A company is building a RAG application for customer support. The knowledge base includes documents in English, Spanish, and French. Which embedding model should they use from OCI Generative AI to ensure accurate retrieval across all languages?

Accepted Answer

cohere.embed-multilingual-light-v3.0. The cohere.embed-multilingual-light-v3.0 model is specifically designed to handle multiple languages, including English, Spanish, and French, ensuring accurate semantic retrieval across all three languages in the RAG application. OCI Generative AI offers this multilingual embedding model to support diverse knowledge bases, whereas single-language models would fail to represent non-English text effectively.

Answer

cohere.generate-english-v2:0

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cohere.embed-english-light-v3.0

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cohere.command-r-plus-v1:0