Oracle AI Vector Search Professional Practice Exam 2025: Latest Questions

You are explaining Oracle AI Vector Search to a team new to vector databases. Which statement best describes what a vector embedding represents in a vector search solution?

A developer runs a similarity search but gets errors indicating dimension mismatch between stored vectors and the query vector. What is the most likely cause?

You are designing a RAG (retrieval-augmented generation) workflow that stores document chunks and their embeddings in Oracle Database. Which additional metadata is most useful to store alongside each chunk to improve answer quality and traceability?

When tuning a similarity query, you want to reduce the number of candidate vectors considered before ranking the top results. Which SQL pattern is typically most effective?

A search application must support multiple tenants in the same schema. Each tenant should only search within its own documents while using a shared embeddings table. What is the best approach?

Your team wants to use vector search for semantic similarity, but also needs keyword constraints (for example, must contain a product code) and the ability to boost results based on recency. What is the most appropriate overall query strategy?

A customer reports that similarity search results look "random" between runs for the same query vector, especially around the last few results in top-N. The dataset is large and uses approximate nearest neighbor indexing. What is the most likely explanation?

You ingest new documents continuously and immediately embed them. Users complain that newly ingested content is sometimes missing from similarity results until later. Which is the most plausible cause in an indexed vector search design?

A team wants to maximize semantic relevance using cosine similarity, but the chosen embedding model sometimes produces vectors with varying magnitudes. What is the best practice to ensure cosine similarity behaves as intended across all stored and query vectors?

You operate a large-scale vector search system where users filter by multiple attributes (tenant, language, document type) and request top-20 similar chunks. Performance degrades significantly during peak load. Which tuning approach is most likely to improve throughput without changing result semantics?

You are onboarding a team to Oracle AI Vector Search. They ask what a vector embedding represents in the context of similarity search. Which statement is most accurate?

A developer stores document embeddings in a VECTOR column and wants to perform similarity search. Which query pattern best matches the intended approach for vector search in Oracle Database?

A team is designing a Retrieval-Augmented Generation (RAG) solution. Which component is primarily responsible for retrieving relevant context chunks using embeddings before prompting the LLM?

You run a similarity query and notice results are inconsistent across runs for the same input vector when using an approximate vector index. Users complain that the top result sometimes changes. What is the most likely explanation?

A retailer stores product descriptions and wants to support multi-language search (customers search in Spanish but products are described in English). What is the best practice for achieving this using embeddings?

After switching from exact brute-force vector comparisons to a vector index, query latency improves but recall drops noticeably (relevant items sometimes missing from top-N). Which tuning action is most appropriate?

A data pipeline loads new documents hourly. You generate embeddings and insert them into a table with a vector index. Over time, query performance degrades and index memory usage grows. What is the most likely operational cause and best corrective action?

You need a hybrid search experience: users want results that are semantically similar to the query but must also satisfy strict filters (for example, category, region, and in-stock). Which approach is recommended?

A regulated enterprise must ensure that only authorized users can query embeddings derived from sensitive documents. Which design best satisfies least privilege while still enabling vector search?

You are troubleshooting poor relevance in a RAG application. The vector search returns chunks that are topically related but do not answer the user’s question precisely. Which change is most likely to improve answerability without changing the LLM?

You are building a RAG workflow in Oracle Database using AI Vector Search. You must ensure the same embedding model is used both when storing vectors and when generating query vectors, otherwise similarity results become unreliable. Which practice best enforces this consistency?

A query that orders results by vector similarity is returning the correct rows but is slower than expected. The execution plan shows a full table scan on the table containing vectors. Which action is the most appropriate next step to improve performance?

You ingest documents and generate embeddings in a pipeline. Occasionally, inserts fail with an error indicating a vector dimension mismatch. The source of embeddings is not guaranteed to be consistent across all documents. What is the best way to prevent these runtime failures?

A team wants to blend semantic similarity (vector search) with structured filters such as tenant_id, region, and document_type. They currently run vector search first and then filter the returned rows in application code, but results sometimes include too few matches after filtering. What approach is recommended?

After enabling a vector index, the team notices that some recently inserted documents are not appearing in similarity search results until much later. Inserts are committed successfully. Which explanation is most likely, and what is the correct remediation?