1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 3 — 15 Questions

Question 1

A development team notices that their RAG application returns responses slowly when processing large PDF documents (100+ pages). They need to improve response time without significantly reducing retrieval quality. Which action is most effective?

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

Accepted Answer

Use a smaller chunk size during document ingestion. Using a smaller chunk size during document ingestion reduces the amount of text per chunk, which decreases the latency of embedding generation and vector search. For large PDFs, smaller chunks also minimize the risk of retrieving overly large context windows that slow down the LLM's response generation, while still preserving retrieval quality if overlap is properly configured.

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Add a reranking step after initial retrieval

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Increase the topK parameter to retrieve more context

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Switch to a larger embedding model for better accuracy

Question 2

An AI engineer observes that the RAG application fails to retrieve relevant documents for certain user queries, despite having a comprehensive knowledge base. The issue appears to be a semantic gap between query phrasing and document content. Which technique should the engineer implement first to address this?

Accepted Answer

Apply query expansion techniques before embedding the user query. Query expansion techniques (e.g., synonym injection, back-translation, or LLM-based paraphrasing) directly address the semantic gap by enriching the user query with alternative phrasings before embedding. This increases the likelihood of matching relevant document chunks in the vector space, without altering the retrieval architecture or requiring additional inference steps.

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Switch from dense to sparse vector embeddings

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Implement a re-ranking model to reorder retrieved results

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Increase the chunk overlap to ensure more context

Question 3

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.

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cohere.generate

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cohere.summarize

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cohere.classify

Question 4

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.

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Fixed-size chunking with 512 tokens

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Sentence-level chunking

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Character-level chunking

Question 5

Which TWO actions can improve the retrieval accuracy of a RAG system? (Select two.)

Accepted Answer

Use a more accurate embedding model. A more accurate embedding model (e.g., OCI Generative AI's embedding models or Cohere embeddings available in OCI) produces higher-quality vector representations that capture semantic meaning more precisely, directly improving retrieval relevance in a RAG pipeline.

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Use a smaller chunk size for all documents

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Remove stop words from documents before embedding

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Increase the topK parameter significantly

Question 6

Which TWO best practices should be followed when designing a RAG application using OCI GenAI? (Select two.)

Accepted Answer

Use OCI Dedicated AI Cluster for inference to ensure data privacy. OCI Dedicated AI Cluster provides a single-tenant, isolated infrastructure for inference, ensuring that sensitive data processed during RAG workflows does not leave a dedicated environment. This is critical for compliance with data residency and privacy requirements, as the cluster is not shared with other customers, unlike multi-tenant endpoints.

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Batch all user queries to minimize costs

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Store raw documents in the vector database for easy updates

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Store embedding API keys in OCI Vault and rotate frequently

Question 7

Which THREE components are essential in a typical RAG architecture built on OCI? (Select three.)

Accepted Answer

Vector database (e.g., OCI OpenSearch, Autonomous Database). A vector database is a core component in RAG architecture on OCI, enabling efficient storage and retrieval of vector embeddings. OCI OpenSearch and Autonomous Database both support vector search capabilities, which are essential for finding relevant context to augment LLM prompts.

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Data ingestion pipeline with Apache Spark

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Prompt template for system instructions

Question 8

A developer calls the OCI GenAI embedding API as shown in the exhibit. What is the most likely cause of the error?

Accepted Answer

The input string is too long for the embedding model. The error is most likely caused by the input string exceeding the maximum token limit for the embedding model. OCI GenAI embedding models have a fixed context window (e.g., 512 or 1024 tokens), and if the input text is longer than that, the API returns an error. The developer must truncate or chunk the input before calling the API.

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The API key does not have permission to call the endpoint

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The endpoint ID is incorrect

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The model ID is not supported for embedding

Question 9

An IAM policy is shown in the exhibit. A user reports that they cannot call the OCI GenAI embedding API, but they can use OCI AI Language. Which policy statement is missing to allow embedding API access?

Accepted Answer

The policy needs 'use' action on 'oci-generative-ai-family'. The user can already use OCI AI Language, indicating that the existing policy grants access to the 'oci-ai-language-family' resource. To also allow access to the OCI GenAI embedding API, the policy must include a 'use' verb on the 'oci-generative-ai-family' resource type. The 'use' verb is the minimum required permission for invoking inference operations like embedding, while 'manage' would be excessive and 'inspect' is insufficient for API calls.

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The policy needs 'inspect' on 'oci-generative-ai-endpoint'

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The policy needs 'manage' action on 'oci-generative-ai-family'

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The policy needs 'inspect' on 'oci-ai-language-family'

Question 10

The architecture shown in the exhibit is missing a critical component for a RAG pipeline. What step is missing between receiving the user query and searching the vector store?

Accepted Answer

A query embedding step using an embedding model. In a RAG pipeline, the user query must be converted into a vector embedding using the same embedding model that was used to index the documents. Without this query embedding step, the vector store cannot perform a meaningful similarity search because it compares vectors, not raw text. Option B correctly identifies this missing transformation between receiving the query and searching the vector store.

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A document chunking step

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A data masking step for privacy

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A reranker step after retrieval

Question 11

A company is building a RAG application using OCI Generative AI and wants to store embeddings for document retrieval. Which OCI service is most appropriate for storing and querying vector embeddings?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch is the most appropriate service for storing and querying vector embeddings because it natively supports k-nearest neighbor (k-NN) search and vector field types, enabling efficient similarity search over high-dimensional embeddings. This makes it ideal for RAG applications where document retrieval relies on comparing query embeddings against stored document embeddings.

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OCI MySQL Database

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OCI Data Flow

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OCI Object Storage

Question 12

A company uses OCI Data Science to fine-tune an embedding model for a specialized domain. After fine-tuning, the model produces embeddings that are not aligned with the vector index used in OCI OpenSearch. What is the most likely cause?

Accepted Answer

The embedding dimension changed after fine-tuning. Fine-tuning an embedding model can alter the dimensionality of the output embeddings if the model's architecture is modified (e.g., changing the final pooling layer or projection head). OCI OpenSearch vector indexes are built with a fixed dimension; if the fine-tuned model produces embeddings of a different size, they cannot be indexed or queried against the existing index, causing a misalignment. This is the most direct and common cause of such a mismatch.

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The fine-tuning process modified the model architecture

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The fine-tuning dataset was too small

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The vector index was built using a different distance metric than used during fine-tuning

Question 13

During load testing, the RAG application's response time increases significantly. The vector search is performed on millions of vectors. Which optimization would MOST reduce latency?

Accepted Answer

Use approximate nearest neighbor (ANN) search instead of exact. Approximate nearest neighbor (ANN) search algorithms, such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index), trade a small amount of accuracy for a dramatic reduction in latency when searching over millions of vectors. In OpenSearch, ANN is implemented via the k-NN plugin and can reduce query latency from seconds to milliseconds by avoiding an exhaustive scan of all vectors.

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Increase the number of replicas in OpenSearch

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Shard the index by document type

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Use a smaller embedding model

Question 14

A security audit reveals that the RAG application exposes internal documents through the chatbot. The vector search index contains sensitive data. Which action should be taken FIRST to mitigate?

Accepted Answer

Implement access control at the OpenSearch index level. The correct first step is to implement access control at the OpenSearch index level because the core issue is that sensitive internal documents are accessible to unauthorized users through the chatbot. By enforcing index-level permissions (e.g., using OpenSearch's fine-grained access control with roles and users), you can restrict which documents the RAG pipeline can retrieve, directly addressing the security vulnerability at the data source. This is a foundational security measure that should precede any other adjustments.

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Reduce the number of retrieved chunks

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Redact sensitive terms from documents before embedding

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Use a different embedding model

Question 15

What is the primary purpose of chunking documents in a RAG pipeline?

Accepted Answer

To ensure each chunk fits within the model's context window. The primary purpose of chunking documents in a RAG pipeline is to divide large documents into smaller, manageable pieces that each fit within the model's context window. This ensures that the retrieval step can fetch relevant chunks without exceeding the token limit of the LLM, which would otherwise cause truncation or failure to process the input.

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To improve embedding quality

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To speed up training

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To reduce storage costs