Oracle AI Database Administration Professional Practice Exam 2025: Latest Questions

You are enabling AI Vector Search in an Oracle database. A developer asks what should be stored in the vector column to support similarity search. Which is the best answer?

A team wants to implement a Retrieval-Augmented Generation (RAG) pattern using the Oracle database as the retrieval store. Which database capability most directly supports retrieving the most semantically relevant chunks for a user query?

Your security team requires that only specific application schemas can run vector similarity queries against a table containing sensitive embeddings. Which is the most appropriate control?

A DBA is troubleshooting slow vector similarity queries. Which action is most likely to provide an immediate, evidence-based starting point before changing configuration?

You store document chunks with embeddings in a table. Users expect that when a document is updated, search results reflect the new content quickly and consistently. Which approach best balances correctness and operational simplicity?

A data science team wants to train a model using database-resident features while minimizing data movement and ensuring repeatable training datasets. Which design is most appropriate?

A workload runs both traditional OLTP queries and vector similarity searches. After enabling vector search, OLTP latency increases during peak similarity-query traffic. Which is the most appropriate DBA strategy?

A compliance auditor asks how you prevent embeddings derived from sensitive customer notes from being exposed to unauthorized analysts, even if those analysts can query aggregated reports. Which control most directly addresses this requirement?

A vector search table contains embeddings generated by two different models with different dimensionality. The application now returns errors or poor relevance after mixing these embeddings in the same similarity query. What is the best corrective action?

Your organization must support 'right to be forgotten' requests. A customer’s data appears in multiple document chunks whose embeddings are used for retrieval. What is the most robust approach to ensure compliance while maintaining vector search quality?

You create a table that stores a text column and an embedding vector column for AI Vector Search. Which database object is MOST appropriate to speed up similarity search queries (top-K nearest neighbors) over the vector column?

A developer wants to call an external machine learning inference endpoint from within the database, but you must ensure the database can only connect to that specific endpoint and nothing else. Which control BEST enforces this?

You have a retrieval-augmented generation (RAG) workflow: user question → retrieve relevant chunks via vector search → send the chunks to an LLM. What is the PRIMARY purpose of the vector search step?

A vector search workload has become slower after a large bulk load of new embeddings. Queries still return correct results but latency increased noticeably. What is the MOST likely administrative action needed?

Your organization requires that any LLM prompt sent from the database must not include personally identifiable information (PII). You need an approach that is enforceable centrally and works for all applications. What is the BEST solution?

A batch job generates embeddings for documents nightly. Some nights, the job partially fails and reruns, causing duplicate vectors for the same document and chunk. What is the BEST database design approach to prevent duplicates while allowing idempotent reruns?

You are integrating a database-resident ML model into an application. Security requires that only a specific application schema can execute the prediction function, while analysts can query the underlying training data but cannot run predictions. What is the BEST way to enforce this separation?

A retrieval query returns irrelevant results. You discover that a subset of embeddings was generated with a different model than the rest, but they are stored in the same vector column. What is the MOST appropriate remediation?

You must support vector search across two regions for low-latency reads. Writes (new documents and embeddings) occur in Region A, but applications in both regions need near-real-time vector search results. Which architecture is MOST appropriate?

A regulated workload requires you to prove which source documents were used to produce each generated answer (lineage) and to support audits later. In a RAG system implemented in the database, what should you store to meet this requirement MOST effectively?

An application uses AI Vector Search and runs a query that combines a structured filter (STATUS='ACTIVE') with a vector similarity predicate. Users report the query is slow after a large batch load. As the DBA, what is the most likely first action to restore predictable performance?

A data science team trains a model in the database using in-database machine learning. You need to ensure the model can be audited and reproduced later, including who created it and with what training data definition. Which approach best supports governance and reproducibility?

You are designing an architecture for a RAG (retrieval-augmented generation) pipeline using Oracle Database. New documents arrive continuously, and the business requires that search results reflect new data quickly without blocking OLTP workloads. Which design is the best practice?

A regulated customer wants to expose vector similarity search results to a service account, but must prevent the account from reading raw document text columns due to confidentiality. Which database-level control most directly enforces this requirement?

After deploying AI Vector Search, a team complains that similarity results are inconsistent between two environments (same data, same queries). Investigation shows one environment uses a different embedding model than the other. What is the most robust way to prevent this class of issue in production?