1Z0-1127 Building LLM Applications with RAG and Vector Search Practice Test 2 — 15 Questions

Question 1

A retail company uses OCI Generative AI Service to build a RAG chatbot for product recommendations. The chatbot should consider both the user's query and the retrieved product descriptions. Which component of the RAG pipeline is responsible for combining these inputs before sending to the LLM?

Accepted Answer

Prompt template. The prompt template is the component in a RAG pipeline that structures the final input to the LLM by combining the user's query with the retrieved product descriptions. It defines the format and instructions (e.g., 'Based on these product descriptions, recommend...') that the LLM uses to generate a coherent response. Without a prompt template, the raw query and documents would be sent without context, leading to poor or irrelevant outputs.

Answer

Reranker

Answer

Document retriever

Answer

Embedding model

Question 2

Which TWO actions are best practices when deploying a RAG application using OCI OpenSearch and OCI Generative AI?

Accepted Answer

Implement a reranker to improve the relevance of retrieved documents.. Option B is correct because implementing a reranker improves retrieval precision by re-scoring the top-k documents from the initial vector search using a cross-encoder model, which captures deeper semantic relevance than cosine similarity alone. In OCI OpenSearch, this is typically done via a post-processing step with OCI Generative AI or a dedicated reranking model, ensuring only the most contextually relevant chunks are passed to the LLM for generation.

Answer

Embed every document chunk in real-time during query processing.

Answer

Use very small chunk sizes (e.g., 50 tokens) to maximize granularity.

Answer

Set the LLM temperature to 1.5 to encourage diverse outputs.

Question 3

Which THREE factors should be considered when designing a vector search index for a RAG application that supports multiple languages?

Accepted Answer

Implement language identification as a preprocessing step.. Option A is correct because language identification as a preprocessing step ensures that documents are correctly tagged before indexing, which allows the system to apply appropriate language-specific tokenization, stop-word removal, and stemming. This prevents cross-language contamination in the vector index and improves retrieval accuracy for a multilingual RAG application.

Answer

Create separate vector indexes for each language.

Answer

Use larger chunk sizes for languages with complex morphology.

Question 4

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

Accepted Answer

The API rate limit has been exceeded; wait for the retry period and implement exponential backoff.. The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

Answer

The model is deprecated; update to the latest model.

Answer

The endpoint URL is incorrect; verify the OCI region endpoint.

Answer

The request payload is malformed; check the input format.

Question 5

An engineer configured the above index mapping for vector search. When performing a k-NN search, the results are unexpected. What is the most likely issue?

Accepted Answer

The space type at the index level and mapping level are mismatched.. Option D is correct because OpenSearch requires the space type to be consistently defined at both the index-level settings (method.parameters.space_type) and the field-level mapping (space_type). A mismatch between these two causes the k-NN search to behave unexpectedly, as the engine uses the index-level setting for distance computation while the mapping-level setting may be used for validation or other purposes.

Answer

The space type 'cosinesimil' is not supported; it should be 'cosine'.

Answer

The dimension 768 does not match the embedding model's output dimension.

Answer

The mapping uses 'knn_vector' type with 'faiss' engine, which is incompatible.

Question 6

You are a cloud architect at a global e-commerce company. The company is building a RAG-based product support chatbot using OCI Generative AI Service and OCI OpenSearch. The chatbot must answer customer questions in real-time by retrieving from a product knowledge base containing over 10 million documents. The current architecture uses a single vector index with all documents, and the LLM (Cohere Command R+) returns answers in English only. The team observes that queries from non-English customers often return irrelevant results, and the chatbot sometimes fails to generate answers within the 5-second SLA. The leadership wants to support 10 languages and reduce the average response time to under 3 seconds. You need to propose a solution that improves both relevance and latency. Which course of action should you take?

Accepted Answer

Replace the embedding model with a multilingual model and partition the vector index by language to reduce search space.. Option B is correct because partitioning the vector index by language reduces the search space for each query, directly improving retrieval latency, while using a multilingual embedding model ensures that non-English queries are semantically matched to documents in their original language, improving relevance. This combination addresses both the 3-second SLA and the 10-language requirement without relying on translation, which introduces latency and potential loss of meaning.

Answer

Increase the number of OCI OpenSearch nodes and upgrade the LLM to a faster variant.

Answer

Translate all non-English queries to English before retrieval and use an English-only embedding model.

Answer

Implement a caching layer for frequent queries and use a larger LLM for better accuracy.

Question 7

You are a data scientist at a legal firm. The firm uses OCR to digitize court documents and then indexes them in OCI OpenSearch for a RAG application. The application uses OCI Generative AI Service (Cohere Command) to answer questions about case law. Recently, the team noticed that the answers are often factually incorrect or include information not present in the retrieved documents. After reviewing the pipeline, you find that the chunking strategy splits documents into 512-token chunks with 128-token overlap. The embedding model is Cohere Embed v3 (English), and the retrieval returns the top 5 chunks. The LLM has a context window of 4096 tokens. The team suspects that the chunking strategy is causing loss of context. What is the best course of action to improve answer accuracy?

Accepted Answer

Increase the chunk size to 1024 tokens and overlap to 256 tokens.. Increasing the chunk size to 1024 tokens and overlap to 256 tokens directly addresses the loss of context by ensuring each chunk contains more complete semantic units (e.g., entire paragraphs or legal arguments) while the larger overlap preserves continuity across chunk boundaries. This improves the quality of the embeddings and the relevance of retrieved chunks, leading to more factually accurate answers from the LLM.

Answer

Reduce the chunk overlap to 64 tokens to avoid redundancy.

Answer

Switch to a smaller LLM with a larger context window.

Answer

Increase the number of retrieved chunks from 5 to 10.

Question 8

A healthcare company is building a RAG-based chatbot to answer patient queries using medical documents stored in OCI Object Storage. They use OCI Generative AI service with Cohere Command R+ model and OCI OpenSearch as the vector database. The chatbot is deployed on OCI Compute with a Flask application. After deployment, the latency for each query is 15-20 seconds, which is unacceptable. Logs show that the embedding generation step (using OCI Generative AI embedding API) takes 8-10 seconds, and the vector search in OpenSearch takes 5-7 seconds. The team has already enabled connection pooling and increased the compute instance shape to the maximum allowed. Which action would MOST effectively reduce the overall latency?

Accepted Answer

Pre-generate embeddings for all documents during ingestion and store them in the vector database, so at query time only the query embedding is generated and compared.. The primary bottleneck is the embedding generation step (8-10 seconds). By pre-generating embeddings for all documents during ingestion and storing them in the vector database, the query-time embedding generation is eliminated, reducing the per-query latency to only the time needed to generate the query embedding and perform the vector search. This directly addresses the largest contributor to the 15-20 second latency.

Answer

Implement a caching layer with Redis to store previous query results and serve cached responses for identical queries.

Answer

Reindex the OpenSearch vector index with optimal settings (e.g., HNSW algorithm, ef_search param) to speed up vector search.

Answer

Switch to a faster embedding model like Cohere Embed v3 (English) which has lower latency.

Question 9

A developer is building a RAG chatbot for an internal knowledge base. To ensure the system retrieves the most relevant chunks, what is the best practice for chunking?

Accepted Answer

Use semantic chunking with overlap. Semantic chunking with overlap ensures that context is preserved and retrieval is more accurate by avoiding splits in meaningful content.

Answer

Use very small chunks

Answer

Use fixed-size chunks without overlap

Answer

Use random-sized chunks

Question 10

A company uses OCI Generative AI to create embeddings for a vector search. They notice high latency in search queries. What is one possible optimization?

Accepted Answer

Use exact search for better accuracy. Approximate nearest neighbor (ANN) search trades a slight reduction in accuracy for a significant speedup, addressing high latency.

Answer

Decrease batch size for embedding creation

Answer

Use approximate nearest neighbor (ANN) search

Answer

Increase the embedding dimension

Question 11

An application uses RAG to answer customer queries, but answers are often incomplete because the retrieved chunks do not contain full context. Which adjustment should the developer make?

Accepted Answer

Increase the number of retrieved chunks. Increasing the number of retrieved chunks gives the model more contextual information, leading to more complete answers.

Answer

Use a different embedding model

Answer

Increase chunk overlap

Answer

Decrease chunk size

Question 12

A team uses OCI OpenSearch as a vector database for RAG. Some queries return no results despite relevant documents being indexed. What is a likely cause?

Accepted Answer

The number of candidates (k) is set too low. If k (the number of nearest neighbors to return) is set too low, the search may not find any documents within the top k, returning no results.

Answer

The vector index is not refreshed after adding documents

Answer

The query text is too long for the embedding model

Answer

The embedding model is incompatible with the document language

Question 13

A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?

Accepted Answer

Cache frequent queries and their embeddings. Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.

Answer

Use a larger generation model

Answer

Reduce chunk size to decrease embedding calls

Answer

Use a larger embedding model for better accuracy

Question 14

An enterprise RAG system must ensure that retrieved data comes only from authorized sources. Which OCI feature should be used to enforce this?

Accepted Answer

OCI IAM policies for the vector database. OCI IAM policies allow fine-grained access control to resources, ensuring only authorized users or services can retrieve data from the vector database.

Answer

Data encryption at rest

Answer

Network security groups

Answer

Resource quotas

Question 15

A team fine-tunes an embedding model for a legal document RAG system but observes low retrieval recall. Which technique is most likely to improve recall?

Accepted Answer

Use hard negative mining during training. Hard negative mining exposes the model to challenging negatives during training, which sharpens the embedding space and improves recall.

Answer

Use a smaller batch size

Answer

Reduce the learning rate

Answer

Increase the number of fine-tuning epochs