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

Question 1

You are a cloud architect at a global e-commerce company. The company is building a RAG-based product support chatbot using OCI Generative AI Service and OCI OpenSearch. The chatbot must answer customer questions in real-time by retrieving from a product knowledge base containing over 10 million documents. The current architecture uses a single vector index with all documents, and the LLM (Cohere Command R+) returns answers in English only. The team observes that queries from non-English customers often return irrelevant results, and the chatbot sometimes fails to generate answers within the 5-second SLA. The leadership wants to support 10 languages and reduce the average response time to under 3 seconds. You need to propose a solution that improves both relevance and latency. Which course of action should you take?

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

Accepted Answer

Replace the embedding model with a multilingual model and partition the vector index by language to reduce search space.. Partitioning the vector index by language reduces the search space for each query, directly improving retrieval latency, while using a multilingual embedding model ensures that non-English queries are semantically matched to documents in their original language, improving relevance. This combination addresses both the 3-second SLA and the 10-language requirement without relying on translation, which introduces latency and potential loss of meaning.

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Increase the number of OCI OpenSearch nodes and upgrade the LLM to a faster variant.

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Translate all non-English queries to English before retrieval and use an English-only embedding model.

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Implement a caching layer for frequent queries and use a larger LLM for better accuracy.

Question 2

You are a data scientist at a legal firm. The firm uses OCR to digitize court documents and then indexes them in OCI OpenSearch for a RAG application. The application uses OCI Generative AI Service (Cohere Command) to answer questions about case law. Recently, the team noticed that the answers are often factually incorrect or include information not present in the retrieved documents. After reviewing the pipeline, you find that the chunking strategy splits documents into 512-token chunks with 128-token overlap. The embedding model is Cohere Embed v3 (English), and the retrieval returns the top 5 chunks. The LLM has a context window of 4096 tokens. The team suspects that the chunking strategy is causing loss of context. What is the best course of action to improve answer accuracy?

Accepted Answer

Increase the chunk size to 1024 tokens and overlap to 256 tokens.. Increasing the chunk size to 1024 tokens and overlap to 256 tokens directly addresses the loss of context by ensuring each chunk contains more complete semantic units (e.g., entire paragraphs or legal arguments) while the larger overlap preserves continuity across chunk boundaries. This improves the quality of the embeddings and the relevance of retrieved chunks, leading to more factually accurate answers from the LLM.

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Reduce the chunk overlap to 64 tokens to avoid redundancy.

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Switch to a smaller LLM with a larger context window.

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Increase the number of retrieved chunks from 5 to 10.

Question 3

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 4

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 5

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.

Answer

Use a different embedding model

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Increase chunk overlap

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Decrease chunk size

Question 6

A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?

Accepted Answer

Cache frequent queries and their embeddings. Caching frequent queries and their embeddings directly reduces the number of API calls to OCI Generative AI, which is the primary cost driver for RAG applications. By storing previously computed embeddings and generated responses, repeated or similar queries avoid redundant embedding and generation operations, leading to significant cost savings. This strategy is especially effective in scenarios with high query repetition or common user intents, as it minimizes both latency and expense without sacrificing accuracy.

Answer

Use a larger generation model

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Reduce chunk size to decrease embedding calls

Answer

Use a larger embedding model for better accuracy

Question 7

An enterprise RAG system must ensure that retrieved data comes only from authorized sources. Which OCI feature should be used to enforce this?

Accepted Answer

OCI IAM policies for the vector database. OCI IAM policies allow you to define granular access controls on the vector database, ensuring that only authorized principals (users, groups, or service principals) can read or write data. This directly enforces that retrieved data comes only from authorized sources, which is a core requirement for enterprise RAG systems.

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Data encryption at rest

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Network security groups

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Resource quotas

Question 8

An application mixes RAG with other data sources. The vector search returns too many irrelevant chunks. What is the best approach to filter them?

Accepted Answer

Use a reranker model. A reranker model (Option A) is the best approach because it takes the initial set of retrieved chunks and re-orders them based on semantic relevance to the query, effectively filtering out irrelevant chunks. Unlike simple vector similarity, a reranker uses cross-encoding to evaluate the query-chunk pair as a whole, which significantly improves precision when mixing RAG with other data sources.

Answer

Use exact search instead of ANN

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Reduce the number of retrieved chunks

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Increase chunk size

Question 9

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 10

Which TWO of the following are best practices for building a RAG pipeline in OCI?

Accepted Answer

Use overlapping chunks. Overlapping chunks ensure that context is not lost at chunk boundaries, which is critical for retrieval accuracy in RAG pipelines. By including overlapping text segments, the embedding model can capture semantic continuity, reducing the risk of missing relevant information when a query spans chunk edges.

Answer

Always use exact vector search for accuracy

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Avoid storing metadata alongside vectors

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Use a single large chunk for each document

Question 11

Which THREE techniques effectively reduce query latency in a RAG system?

Accepted Answer

Pre-compute embeddings for all documents. Option A (pre-compute embeddings for all documents) is correct because it removes the embedding-model inference cost from the query path: documents are embedded once at ingestion time, so at query time only the user query must be embedded, cutting per-request latency. Option B (use approximate nearest neighbor search) is correct because ANN indexes such as HNSW or IVF trade a small amount of recall for sublinear search, avoiding the exhaustive exact k-NN scan over millions of vectors and dramatically lowering retrieval latency. Option E (use a smaller embedding model) is correct because a lower-dimensional, lighter encoder produces query vectors faster and yields smaller vectors that reduce distance-computation and index-lookup time, directly lowering latency. Option C (use a larger generation model) does not belong because a bigger LLM increases token-generation time and thus raises, not reduces, end-to-end latency. Option D (increase the number of shards) does not belong because more shards add fan-out, coordination, and merge overhead across nodes, which typically increases query latency rather than reducing it.

Answer

Use a larger generation model

Answer

Increase the number of shards

Question 12

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 13

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.

Answer

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 14

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

Question 15

During a RAG implementation, the response quality degrades because the LLM receives too many irrelevant document chunks. Which technique can best filter out irrelevant chunks before sending them to the LLM?

Accepted Answer

Implement a reranking step using a cross-encoder model.. A reranking step using a cross-encoder model (Option C) directly addresses the problem of irrelevant chunks by scoring each retrieved chunk against the user query with a deep, pairwise similarity computation. Unlike a bi-encoder used in initial retrieval, a cross-encoder evaluates query-chunk pairs jointly, allowing it to reorder or filter out chunks that are semantically irrelevant, thus improving the quality of context fed to the LLM.

Answer

Use a larger LLM for generation, hoping it ignores irrelevant chunks.

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Reduce the top-k retrieval count.

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Increase the chunk size to provide more context.