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

Question 1

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

Answer

Reduce the number of retrieved chunks

Answer

Increase chunk size

Question 2

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 with clear instructions to use only the provided context is a direct and effective way to reduce hallucination.

Answer

Use a larger retrieval chunk size

Answer

Increase the temperature

Answer

Decrease the maximum token length

Question 3

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

Accepted Answer

Use overlapping chunks. Overlapping chunks improve context preservation, and using a pre-trained embedding model provides a strong baseline for retrieval.

Answer

Always use exact vector search for accuracy

Answer

Avoid storing metadata alongside vectors

Answer

Use a single large chunk for each document

Question 4

Which THREE of the following are likely causes if retrieval returns no results despite documents being indexed in an OCI OpenSearch vector store?

Accepted Answer

The k-NN algorithm is misconfigured (e.g., k=0). An unbuilt/refreshed index, a misconfigured k-NN algorithm, and an incorrect connection string are common causes of empty retrieval results.

Answer

The embedding model dimension mismatch

Answer

The query embedding is out of distribution

Question 5

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

Accepted Answer

Pre-compute embeddings for all documents. Pre-computing embeddings for all documents eliminates the need to generate embeddings at query time, which is a computationally expensive step. By storing pre-computed vector representations, the system can directly perform similarity searches against the index, significantly reducing latency.

Answer

Use a larger generation model

Answer

Increase the number of shards

Question 6

Refer to the exhibit. Why did the embedding creation fail?

Accepted Answer

The API call was not properly authenticated. The error 'MissingAuthenticationError' clearly indicates that the API call lacks authentication credentials, which is required for OCI API calls.

Answer

The input text is too short

Answer

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

Answer

The region is not enabled for the Generative AI service

Question 7

Refer to the exhibit. What is a potential issue with this OCI OpenSearch index template configuration?

Accepted Answer

The space_type in settings (l2) differs from the method's space_type (cosinesimil). The space_type defined at the index settings (l2) conflicts with the space_type defined in the method mapping (cosinesimil). This mismatch can lead to incorrect distance calculations and poor retrieval results.

Answer

The ef_construction parameter is set too low

Answer

Number of replicas is 0, which provides no redundancy

Answer

The dimension 1024 is too large for the knn_vector type

Question 8

Refer to the exhibit. What is the best action to resolve this error?

Accepted Answer

Reduce the number of retrieved documents. The input exceeds the model's context length due to a high number of retrieved document tokens. Reducing the number of documents retrieved (or their size) is the most direct fix.

Answer

Decrease the temperature of the generation model

Answer

Increase the max_tokens parameter for generation

Answer

Use a smaller chunk size for documents

Question 9

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. Option B is correct because the cohere.embed-multilingual-light-v3.0 model supports multiple languages, making it suitable for multilingual retrieval. Option A is wrong because it is English-only. Option C is wrong because it is Cohere's command model, not an embedding model. Option D is wrong because it is a text generation model, not an embedding model.

Answer

cohere.generate-english-v2:0

Answer

cohere.embed-english-light-v3.0

Answer

cohere.command-r-plus-v1:0

Question 10

An organization is experiencing low recall in their RAG system. They are using OCI OpenSearch as the vector store with cosine similarity. After reviewing the retrieved chunks, they notice that relevant documents are not being returned. Which configuration change is most likely to improve recall?

Accepted Answer

Reduce the chunk size to capture more granular information.. Option A is correct because reducing the chunk size increases the number of chunks and can capture more fine-grained information, improving recall at the cost of precision. Option B is wrong because increasing chunk size may reduce recall by missing details. Option C is wrong because switching to Euclidean distance does not inherently improve recall. Option D is wrong because using a deterministic ID generator does not affect retrieval quality.

Answer

Use a deterministic ID generator for consistent chunk IDs.

Answer

Increase the chunk size to provide more context per chunk.

Answer

Switch similarity metric from cosine to Euclidean distance.

Question 11

A healthcare company is deploying a RAG application using OCI Generative AI and wants to ensure patient data privacy. They cannot send sensitive data to a public embedding endpoint. Which approach should they take to embed documents while maintaining data residency and security?

Accepted Answer

Provision a dedicated AI cluster in their OCI tenancy to host the embedding model.. Option C is correct because OCI allows deploying Cohere models as dedicated AI clusters, ensuring data does not leave the customer's tenancy. Option A is wrong because the public endpoint processes data in Oracle's shared infrastructure. Option B is wrong because using a third-party API violates data residency. Option D is wrong because hashing before embedding destroys semantic meaning.

Answer

Use the standard OCI Generative AI public endpoint with data encryption in transit.

Answer

Use an external embedding service that complies with HIPAA in a different cloud region.

Answer

Hash the document text before sending to the public embedding endpoint.

Question 12

A developer is building a RAG pipeline using OCI Data Science and wants to store vector embeddings. Which OCI service is optimized for vector search and can be used as a vector store?

Accepted Answer

OCI OpenSearch. Option B is correct because OCI OpenSearch provides a vector engine that supports k-NN search. Option A is wrong because OCI Object Storage is not a searchable vector store. Option C is wrong because OCI Autonomous Database has vector capabilities but is not primarily optimized for vector search at scale. Option D is wrong because OCI Streams is for streaming data.

Answer

OCI Autonomous Database

Answer

OCI Object Storage

Answer

OCI Streaming

Question 13

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.. Option D is correct because reranking with a cross-encoder is a common post-retrieval step to improve relevance. Option A is wrong because increasing chunk size may include more noise. Option B is wrong because using a larger LLM does not filter irrelevant chunks. Option C is wrong because reducing top-k lowers chance of including relevant ones too.

Answer

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

Answer

Reduce the top-k retrieval count.

Answer

Increase the chunk size to provide more context.

Question 14

A company is using Oracle Database 23ai AI Vector Search for their RAG pipeline. They notice that similarity search often returns chunks that are semantically unrelated but syntactically similar due to token overlap. Which vector index type should they consider to improve semantic relevance?

Accepted Answer

HNSW index. Option C is correct because the Hierarchical Navigable Small World (HNSW) index is more effective for semantic search than IVF indices because it preserves global graph structure. Option A is wrong because IVF_FLAT uses inverted files and may suffer from token overlap bias. Option B is wrong because IVF_SQ8 is a quantized version of IVF, not better for semantics. Option D is wrong because the default index is often IVF_FLAT.

Answer

IVF_SQ8 index

Answer

IVF_FLAT index

Answer

Use the default index type, which is IVF_FLAT

Question 15

A developer is testing a RAG application using OCI Generative AI. They receive an error: 'The model cohere.command-r-plus-v1:0 is not supported in this region.' What is the most likely cause?

Accepted Answer

The model is not available in the selected OCI region.. The error message explicitly states that the model 'cohere.command-r-plus-v1:0' is not supported in the region. OCI Generative AI models are region-specific; each model is deployed only in certain OCI regions (e.g., us-ashburn-1, eu-frankfurt-1). If the selected region does not host that model, the API returns this error regardless of endpoint formatting, tenancy configuration, or model name spelling.

Answer

The endpoint URL is incorrectly formatted.

Answer

The tenancy is in a different availability domain.

Answer

The model name has a typo.