1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 7 — 15 Questions

Question 1

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?

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

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.

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

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Reduce the number of neighbor partitions to 2.

Question 2

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.. Enabling reranking improves the relevance of retrieved documents by reordering them based on semantic match with the query. Without reranking, the initial vector search results may not be optimally ordered.

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The embedding model is not suited for the domain.

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The top K value is set too high.

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

Question 3

An organization needs to extract text from PDF documents and convert them into embeddings for a RAG pipeline using OCI. Which OCI service is best suited for extracting text from PDFs?

Accepted Answer

OCI Document Understanding. OCI Document Understanding is purpose-built for extracting text, tables, and key-value pairs from PDFs and images using pre-trained AI models. It directly supports the text extraction step required to prepare documents for embedding generation in a RAG pipeline, unlike the other services which focus on different modalities or lack native PDF text extraction capabilities.

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OCI Language

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OCI Speech

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OCI Vision

Question 4

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.

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OCI MySQL HeatWave

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OCI Database (Autonomous Database)

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OCI Object Storage

Question 5

What is the primary purpose of an embedding model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors.. The primary purpose of an embedding model in a RAG pipeline is to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors enable the retrieval component to efficiently find relevant documents by measuring similarity (e.g., cosine similarity) between the query and stored document embeddings. Without this conversion, the system cannot perform semantic search over unstructured text.

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To generate human-like responses.

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To rank search results.

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To summarize long documents.

Question 6

A RAG system returns irrelevant chunks even though the embedding model and vector index are correctly configured. After reviewing, the chunks are too large and contain extraneous information. Which combination of adjustments should be made to improve relevance?

Accepted Answer

Reduce chunk size, increase overlap, and adjust topK.. Reducing chunk size removes extraneous information, increasing overlap ensures context continuity across smaller chunks, and adjusting topK limits the number of retrieved chunks to the most relevant ones. This combination directly addresses the problem of large chunks containing irrelevant data while maintaining retrieval precision.

Answer

Increase chunk overlap only.

Answer

Decrease chunk size and increase chunk overlap.

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Use semantic chunking and adjust topK.

Question 7

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

Accepted Answer

OCI Search with OpenSearch. A RAG system needs a way to parse documents into chunks (OCI Document Understanding) and a vector store to index and search embeddings (OCI Search with OpenSearch).

Answer

OCI Object Storage

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OCI Functions

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OCI Data Flow

Question 8

Which THREE factors directly influence the quality of responses in a RAG system? (Choose three.)

Accepted Answer

The prompt template used to ask the LLM. The prompt template directly controls how the LLM interprets the retrieved context and formulates its response. A well-structured prompt with clear instructions, context formatting, and output constraints significantly improves response relevance and accuracy, while a poorly designed prompt can lead to hallucinations or off-topic answers.

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The temperature parameter of the LLM

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The number of GPUs allocated to the LLM

Question 9

A developer wants to build a RAG application that processes highly sensitive medical records. The documents are already stored in OCI Object Storage. Which vector storage strategy best balances security and performance?

Accepted Answer

Use OCI OpenSearch with a private subnet and VCN security lists. It uses OCI OpenSearch deployed within a private subnet, which ensures that vector data never traverses the public internet, while VCN security lists provide granular traffic control. This architecture balances security (data isolation and access control) with performance (low-latency access within the same VCN or via FastConnect/IPSEC VPN) for sensitive medical records.

Answer

Store vectors in-memory within the application server

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Use OCI OpenSearch with a public endpoint for low latency

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Use a third-party vector database outside OCI

Question 10

A company's RAG application ingests news articles that are updated frequently. The vector store in OCI OpenSearch contains embeddings of the articles. The team notices that outdated information is still retrieved even after updating the source documents. What is the most effective way to ensure the vector store reflects the latest content?

Accepted Answer

Use the OCI OpenSearch document update API to replace embeddings for changed documents. Using the OCI OpenSearch document update API to replace embeddings for changed documents is efficient and targeted, ensuring immediate consistency.

Answer

Increase the TTL for vector indices

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Rely on the LLM to ignore outdated information

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Re-index the entire vector store daily

Question 11

A legal firm needs an AI assistant that can answer questions based on a large corpus of internal regulations that change quarterly. The firm also requires high accuracy and the ability to cite sources. Which approach should the firm choose?

Accepted Answer

Build a RAG application with vector search and citation generation. Retrieval-Augmented Generation (RAG) with vector search allows the legal firm to index its quarterly-changing regulations into a vector database, retrieve the most relevant chunks for each query, and generate answers with source citations. This approach ensures high accuracy by grounding the LLM's output in the current, authoritative documents without requiring retraining, and citation generation provides the necessary source traceability for legal compliance.

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Use a pre-trained model without customization

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Implement a rule-based search engine

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Fine-tune a pre-trained model on the current regulations

Question 12

When invoking the OCI Generative AI service from a RAG application, the developer receives a 401 Unauthorized error. The application uses resource principal authentication from an OCI Data Science notebook session. What is the most likely fix?

Accepted Answer

Ensure the dynamic group includes the data science notebook session and has the correct policy. The 401 Unauthorized error indicates that the OCI Generative AI service cannot authenticate the request. In a RAG application using resource principal authentication from an OCI Data Science notebook session, the notebook session must be a member of a dynamic group, and that dynamic group must have an IAM policy granting it the necessary permissions (e.g., `allow dynamic-group to use generative-ai-family in compartment `). Option C correctly identifies that the dynamic group configuration or policy is missing or incorrect, which is the most likely cause of the authentication failure.

Answer

Add the Generative AI service to the subnet's security list

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Use an API key instead of resource principal

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Restart the notebook session

Question 13

A document processing pipeline uses OCI Document Understanding to extract text from PDFs, then creates embeddings with OCI Generative AI. Some documents exceed the embedding model's token limit. What is the best approach?

Accepted Answer

Split the document into chunks that fit the limit and embed each chunk separately. Splitting documents into chunks that fit within the embedding model's token limit ensures that no information is lost while still allowing each chunk to be embedded and indexed separately. This approach is standard in RAG pipelines, where documents are chunked to balance token limits and retrieval granularity, enabling the system to retrieve relevant chunks rather than entire documents.

Answer

Truncate the document to the token limit

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Use a different embedding model with a higher token limit

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Skip documents that exceed the limit

Question 14

A team is designing a RAG system for a multilingual knowledge base. Which TWO strategies are appropriate? (Choose two.)

Accepted Answer

Store separate vector indices per language. Storing separate vector indices per language allows the RAG system to optimize retrieval for each language's unique semantic and syntactic characteristics. This avoids cross-language interference and enables the use of language-specific preprocessing, tokenization, and embedding models, which improves retrieval accuracy for multilingual queries.

Answer

Disable vector search for non-English queries

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Translate all documents to English before indexing

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Use a different embedding model per language

Question 15

A developer is troubleshooting low recall in a vector search. Which THREE factors should be checked? (Choose three.)

Accepted Answer

Embedding model quality and relevance to domain. The embedding model's quality and domain relevance directly determine how well semantic relationships are captured. If the model is not fine-tuned on domain-specific data, it may fail to map similar concepts close together in the vector space, leading to low recall. For example, a general-purpose model may not distinguish between 'bank' as a financial institution versus a river bank in a legal document search.

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The number of results returned (k) in the search

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The LLM's temperature setting