1Z0-1127 Building LLM Applications with RAG and Vector Search Practice Test 4 — 15 Questions

Question 1

A team is designing a RAG system for legal document review. They want to ensure that the retrieved chunks are contextually coherent and not truncated mid-sentence. Which chunking strategy should they use?

Accepted Answer

Recursive chunking based on sentence boundaries.. Option B is correct because sentence-based chunking preserves semantic boundaries, avoiding mid-sentence truncation. Option A is wrong because fixed-size chunks often cut sentences. Option C is wrong because paragraph-level may be too large. Option D is wrong because token-level is too fine-grained and loses context.

Answer

Token-level chunking.

Answer

Semantic chunking using document section headers.

Answer

Fixed-size character chunking with overlap.

Question 2

An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?

Accepted Answer

Provide clear instructions in the system prompt to answer only based on the provided context.. Option D is correct because instructing the LLM to only answer based on context reduces hallucinations. Option A is wrong because increasing temperature increases randomness, worsening hallucinations. Option B is wrong because adding more retrieved chunks may introduce conflicting information. Option C is wrong because using a smaller model may increase hallucination.

Answer

Increase the temperature parameter to make outputs more focused.

Answer

Use a smaller LLM to reduce model capacity.

Answer

Retrieve more chunks (increase top-k) to provide more context.

Question 3

Which TWO of the following are best practices when indexing documents for a RAG application using OCI OpenSearch?

Accepted Answer

Enable chunk overlap to avoid splitting important information across chunks.. Options A and D are correct. Using overlapping chunks prevents information loss at boundaries (A). Storing metadata like source document helps with traceability (D). Option B is wrong because stop words removal may harm retrieval for queries containing common words. Option C is wrong because stemming can lose precision in semantic search when using embeddings.

Answer

Use the same chunk size for all documents regardless of content type.

Answer

Apply stemming to reduce vocabulary size.

Answer

Remove all stop words from the text before embedding.

Question 4

Which THREE factors should be considered when choosing a vector store for a RAG application in OCI?

Accepted Answer

Capability of hybrid search (vector + keyword).. Options B, D, and E are correct. Indexing latency (B) affects how fast new documents can be ingested. Multi-tenancy (D) is important for enterprise use. Hybrid search support (E) combines vector and keyword search for better recall. Option A is wrong because CPU architecture is irrelevant. Option C is wrong because vector dimension is determined by the embedding model, not the store.

Answer

The maximum vector dimension supported.

Answer

The CPU architecture of the vector store nodes.

Question 5

Which TWO of the following are valid approaches to serve a RAG application in OCI with low latency?

Accepted Answer

Implement a caching layer for frequently asked questions.. Options C and D are correct. Using an LLM with streaming response (C) reduces perceived latency. Caching common queries (D) avoids repeated retrieval and generation. Option A is wrong because pre-computing all possible answers is impractical. Option B is wrong because increasing chunk size can increase latency due to more tokens to process.

Answer

Pre-compute embeddings and answers for all possible questions.

Answer

Deploy the vector store on multiple regions to reduce network latency.

Answer

Increase the chunk size to reduce the number of retrievals.

Question 6

A developer sends the above request to the OCI Generative AI API. The response returns an error: 'InvalidParameter: The parameter 'topP' is not supported for this model.' What is the most likely reason?

Accepted Answer

The 'cohere.command-r-plus-v1:0' model does not accept the topP parameter.. Option B is correct because the command-r-plus model does not support the topP parameter; it only supports temperature. Option A is wrong because topP is a valid parameter in some models. Option C is wrong because JSON is valid. Option D is wrong because modelId is correct.

Answer

The model ID is deprecated.

Answer

The topP parameter value is out of range.

Answer

The JSON request format is incorrect.

Question 7

An OCI CLI command above returns embeddings for the phrase 'Hello world'. The developer notices that the embedding vector length is 384 dimensions. However, they expected 768 dimensions. What is the most likely cause?

Accepted Answer

The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.. Option B is correct because cohere.embed-multilingual-light-v3.0 outputs 384-dimensional embeddings by default, while the 'v3' version outputs 1024. Option A is wrong because the flag does not affect dimension. Option C is wrong because truncate mode does not change dimension. Option D is wrong because input length is irrelevant.

Answer

The input text 'Hello world' is too short, causing dimension reduction.

Answer

The CLI result is truncated in the display.

Answer

The --truncate END flag reduces the dimension.

Question 8

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?

Accepted Answer

Increase the TARGET ACCURACY value to 99.. Option D is correct because increasing the TARGET ACCURACY parameter forces the index to consider more candidates, improving recall. Option A is wrong because increasing neighbor partitions may improve performance but not necessarily recall. Option B is wrong because changing to HNSW would alter the index type but may require more rebuild. Option C is wrong because reducing neighbor partitions reduces recall.

Answer

Change the index type from IVF to HNSW.

Answer

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

Answer

Reduce the number of neighbor partitions to 2.

Question 9

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.

Answer

The embedding model is not suited for the domain.

Answer

The top K value is set too high.

Answer

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

Question 10

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 specifically designed to extract text and structured data from documents like PDFs, making it the ideal choice for preprocessing.

Answer

OCI Language

Answer

OCI Speech

Answer

OCI Vision

Question 11

A developer implements a RAG chatbot using OCI Generative AI with streaming enabled. The chatbot fails to remember earlier conversation turns during a session. What is the most likely cause?

Accepted Answer

The application does not include previous messages in the request.. To maintain conversation history, the application must explicitly pass previous messages in each request. Without it, the model treats each query independently.

Answer

The max_tokens parameter is set too low.

Answer

The streaming endpoint does not support conversation history.

Answer

The temperature parameter is too high.

Question 12

A data scientist is designing a RAG system with a large vector database (hundreds of millions of documents) and requires high recall accuracy. Which vector search index type should be used in OCI Search with OpenSearch?

Accepted Answer

HNSW (Hierarchical Navigable Small World). HNSW (Hierarchical Navigable Small World) provides excellent recall and speed for large datasets, making it ideal for high-accuracy requirements.

Answer

LSH (Locality Sensitive Hashing)

Answer

Flat (brute-force)

Answer

IVF (Inverted File Index)

Question 13

What is a recommended practice to prevent the LLM from generating information not present in the retrieved context when building a RAG application?

Accepted Answer

Using a system message that says 'Use only the provided context to answer.'. Including a system message that explicitly instructs the model to rely only on the provided context reduces the likelihood of hallucination.

Answer

Setting the temperature to 0.

Answer

Including few-shot examples in the prompt.

Answer

Increasing the topK value.

Question 14

An application using OCI Generative AI returns a 403 Forbidden error when attempting to invoke a model. The user's API key is valid and the endpoint is correct. What is the most likely cause?

Accepted Answer

The IAM policy does not grant the necessary permission to use the model.. OCI requires specific IAM policies to allow users to use Generative AI services. A missing or incorrect policy is the typical cause of 403 errors.

Answer

The model is out of capacity.

Answer

The region is not supported for the selected model.

Answer

The API request body is malformed.

Question 15

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 supports vector indexing and search natively, making it a suitable managed solution for RAG vector storage.

Answer

OCI MySQL HeatWave

Answer

OCI Database (Autonomous Database)

Answer

OCI Object Storage