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

Question 1

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?

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

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.

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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 2

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.

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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 3

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 4

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. The most effective way to ensure the vector store reflects the latest content is to update the embeddings for changed documents using the document update API. This replaces outdated vectors with new ones, ensuring retrieval returns current information.

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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 5

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.

Answer

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 6

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

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Move the vector store to a different region

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Switch to a larger embedding model for better accuracy

Question 7

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 8

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 9

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. Option C is correct because deploying the OCI Generative AI service endpoint in the required region ensures that inference requests and the associated data are processed within that region, satisfying data residency. Option E is correct because using an embedding model hosted in the required region ensures that document and query embeddings are generated locally, so the underlying text data does not leave the region. Options A and D are wrong because enabling cross-region access or replicating the vector store across regions would move or copy data outside the required region, violating residency. Option B is wrong because a global search interface can route queries and data to locations outside the specified region, which is incompatible with strict data residency.

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 10

Refer to the exhibit. A developer runs the command and immediately tries to use the endpoint. The application fails with an error indicating the endpoint is not active. What is the most likely reason?

Accepted Answer

The endpoint is in provisioning state and not yet ready. When a developer creates a new model deployment endpoint in OCI, the endpoint enters a 'provisioning' state and can take several minutes to become active. Attempting to use the endpoint immediately after creation will result in an error indicating the endpoint is not ready, as the underlying infrastructure and model loading must complete before inference requests are accepted.

Answer

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

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The purpose parameter is misspelled

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The compartment ID is incorrect

Question 11

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

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OCI Object Storage

Question 12

A developer needs to generate embeddings for text data using the OCI Generative AI service. Which API should they call to get vector representations of text?

Accepted Answer

cohere.embed. The cohere.embed API is specifically designed to convert text into vector embeddings, which are numerical representations that capture semantic meaning. This is the correct choice for generating embeddings for RAG or vector search workflows in OCI Generative AI.

Answer

cohere.generate

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cohere.summarize

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cohere.classify

Question 13

A healthcare organization plans to deploy a RAG application on OCI that handles sensitive patient data. They require that all LLM inference and embedding processing happen within a controlled environment to avoid data leakage to public endpoints. Which OCI feature should they use?

Accepted Answer

OCI Dedicated AI Cluster. OCI Dedicated AI Cluster provides a fully isolated, single-tenant infrastructure for running AI workloads, including LLM inference and embedding processing. This ensures all data processing occurs within a controlled environment without traversing public endpoints, meeting the healthcare organization's requirement to prevent data leakage of sensitive patient data.

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OCI Data Labeling

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OCI Vault

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OCI Data Masking

Question 14

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

Accepted Answer

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space. OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

Answer

Use OCI Document Understanding to convert images to text, then index text

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Use separate vector stores for text and image embeddings

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Use image captioning to generate text descriptions and index those

Question 15

When chunking a large Python code repository for a RAG application, which chunking strategy is best suited to preserve code semantics and functionality?

Accepted Answer

Semantic chunking based on function and class definitions. Semantic chunking based on function and class definitions preserves code semantics by ensuring that logically cohesive units (e.g., a complete function or class) remain intact. This prevents the RAG system from retrieving fragmented code that would lose context or break syntax, which is critical for accurate code generation or understanding.

Answer

Fixed-size chunking with 512 tokens

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Sentence-level chunking

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Character-level chunking