1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 6 — 15 Questions

Question 1

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.. 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 2

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.. 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 3

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 4

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 5

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 6

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 you to define granular access controls on the vector database, ensuring that only authorized principals (users, groups, or service principals) can read or write data. This directly enforces that retrieved data comes only from authorized sources, which is a core requirement for enterprise RAG systems.

Answer

Data encryption at rest

Answer

Network security groups

Answer

Resource quotas

Question 7

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

Question 8

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 9

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

Accepted Answer

Use overlapping chunks. Overlapping chunks ensure that context is not lost at chunk boundaries, which is critical for retrieval accuracy in RAG pipelines. By including overlapping text segments, the embedding model can capture semantic continuity, reducing the risk of missing relevant information when a query spans chunk edges.

Answer

Always use exact vector search for accuracy

Answer

Avoid storing metadata alongside vectors

Answer

Use a single large chunk for each document

Question 10

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 11

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. B is correct because OCI OpenSearch is a fully managed, search and analytics engine that natively supports k-nearest neighbor (k-NN) search on dense vector embeddings. It provides optimized indexing and querying for high-dimensional vectors, making it the ideal vector store for a RAG pipeline in OCI Data Science.

Answer

OCI Autonomous Database

Answer

OCI Object Storage

Answer

OCI Streaming

Question 12

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.

Question 13

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.. Explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.

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 14

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.. Implementing a caching layer for frequently asked questions reduces redundant LLM invocations and vector store queries, directly lowering latency for repeated queries. This approach leverages in-memory caches like Redis or Memcached to serve precomputed responses, bypassing the retrieval and generation pipeline for cached items.

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 15

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.. The Cohere model 'cohere.embed-multilingual-light-v3.0' is specifically designed to output 384-dimensional embeddings. The developer's expectation of 768 dimensions likely stems from familiarity with larger models like 'cohere.embed-english-v3.0', which outputs 1024 dimensions, or other models that produce 768-dimensional vectors. The embedding dimension is a fixed property of the model, not influenced by input length or CLI display settings.

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.