20+ practice questions focused on Building LLM Applications with RAG and Vector Search — one of the most tested topics on the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127-25 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Building LLM Applications with RAG and Vector Search PracticeA 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?
Explanation: Semantic chunking with overlap ensures that each chunk preserves coherent meaning by splitting text at natural boundaries (e.g., sentences or paragraphs) rather than arbitrary token counts. The overlap allows the retriever to capture context that spans chunk boundaries, which is critical for RAG systems to retrieve the most relevant chunks for the LLM to generate accurate answers.
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?
Explanation: To reduce latency in vector search, using approximate nearest neighbor (ANN) search is the standard optimization. ANN algorithms, such as those available in OCI Vector Search (e.g., HNSW, IVF), trade a small amount of accuracy for a significant reduction in query latency compared to exact search. Option A (decrease batch size) affects embedding creation, not search latency. Option C (exact search) typically increases latency due to exhaustive distance computations. Option D (increase embedding dimension) increases computational overhead and latency.
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?
Explanation: Increasing the number of retrieved chunks (C) directly addresses incomplete answers by providing more context from the vector database. With RAG, the retriever fetches the top-k chunks most similar to the query; raising k increases the breadth of information passed to the LLM, reducing the chance that critical context is missing. This is the most straightforward fix when chunks themselves are correctly sized but the retrieved set lacks full coverage.
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?
Explanation: In OCI OpenSearch, the k parameter (number of nearest neighbors) determines how many candidate vectors are retrieved during a k-NN search. If k is set too low, the search may miss relevant documents even if they are indexed, because the algorithm only returns the top-k closest vectors. Increasing k broadens the search space and improves recall.
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?
Explanation: Prompt engineering that instructs the model to strictly adhere to the provided context is the most effective mitigation because it directly addresses the root cause of hallucination: the model's tendency to generate information beyond the retrieved documents. By explicitly constraining the model's behavior through the system or user prompt, you reduce the likelihood of fabricated facts without altering the retrieval or generation parameters.
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