Oracle AI Database Administration Professional Advanced Practice Exam: Hard Questions 2025

You administer an Oracle Database used for semantic product search with AI Vector Search. After a bulk refresh (tens of millions of rows) into the base table, vector similarity queries suddenly become 10x slower. Conventional B-tree indexes and statistics look fine, and the execution plan still shows a VECTOR INDEX access path. You also notice a spike in segment fragmentation and a large number of deleted/obsolete vectors. What is the BEST remediation approach to restore stable vector search performance while minimizing future refresh impact?

A multi-tenant application stores embeddings for documents in a shared table. Each tenant must be guaranteed that vector search results never leak across tenants, even if developers forget to add tenant filters in SQL. The application also requires high performance for top-K vector queries. Which design best enforces tenant isolation with minimal risk and without sacrificing vector index usage?

A team reports inconsistent semantic search quality: sometimes results are highly relevant, other times clearly unrelated for the same query text. The database stores embeddings produced by two different embedding models over time due to a migration, but the table has only one VECTOR column and one vector index. No errors are raised. What is the MOST likely root cause and the best corrective action?

You are integrating an LLM-based summarization workflow. The app retrieves candidate documents using vector similarity, then sends the top results to an LLM for summarization. You must reduce hallucinations and ensure the LLM only summarizes documents the user is authorized to read. The vector search layer is fast but occasionally returns semantically similar documents from restricted categories due to missing metadata filters in some queries. What is the BEST database-centric control to enforce authorization and reduce hallucination risk in the RAG pipeline?

An ML feature store is implemented inside Oracle Database. Data scientists run automated training jobs that create many temporary feature tables, materialized views, and intermediate results. Over time, the database experiences frequent space pressure in TEMP and UNDO during training windows, causing ORA-01652 and occasional query cancellations. You are asked to stabilize training runs without simply overprovisioning storage. Which approach is MOST effective and aligned with best practices?

A vector search workload uses hybrid retrieval: a structured filter (region, product_status) plus vector similarity (top-K). After a data skew change (one region becomes 90% of data), the hybrid query becomes slow for that region only. The plan shows the database applying the vector similarity first, then filtering by region, causing many wasted distance computations. What is the BEST corrective action to restore performance while preserving result quality?

Your organization mandates auditability for AI-assisted decisions. A stored procedure performs vector retrieval, then calls an in-database ML model to produce a classification score used in credit risk decisions. Auditors require that for any decision you can reconstruct: input features, model identifier, model version/hash, and the retrieved evidence (documents/rows) that influenced the score—without exposing PII to unauthorized staff. Which design BEST satisfies auditability and governance requirements?

A data science team wants to operationalize an in-database ML model. They retrain weekly and want zero-downtime model updates for scoring queries that run continuously. Sometimes a retrain produces a model that performs worse, so they also need fast rollback. Which approach BEST meets these requirements in the database?

An internal team uses an LLM to generate SQL that is executed against an Oracle Database for ad-hoc analytics. After a security review, you must prevent prompt-injected SQL from accessing unauthorized tables, using dangerous packages, or running DDL, while still allowing flexible SELECTs on approved schemas. The team wants controls enforced even if the application layer is compromised. What is the BEST database-side strategy?

A production workload combines in-database ML scoring with heavy OLTP. During peak hours, latency-sensitive OLTP sessions degrade when batch scoring jobs kick off. The scoring jobs are important but can run slightly slower. You need a solution that is predictable, enforceable in the database, and minimizes operational overhead. What is the BEST approach?