50 Oracle AI Database Administration Professional Practice Questions: Question Bank 2025

A retail application stores product descriptions in a table and wants to enable semantic search ("find items like this description") inside Oracle Database. Which object is most directly required to support vector similarity search?

Your team wants to compute text embeddings in the database and keep an audit trail showing who requested embedding generation and for which rows. Which approach best supports governance while minimizing application changes?

A data scientist uses an in-database model to score churn probability nightly. You want the scoring to be repeatable and to avoid unexpected differences between runs. Which practice best improves reproducibility?

You need to store embeddings for documents and frequently filter by tenant_id before running similarity search. Which design best supports both filtering and fast vector search?

After enabling vector search, query latency spikes when users request top-N similar items. The execution plan shows full scans over the embeddings table. What is the most likely administrative fix?

A model training pipeline runs inside the database and uses a table of features derived from transactional data. The pipeline sometimes produces different results because late-arriving transactions change the feature table during training. What is the best database-side approach to stabilize the training dataset?

An AI feature engineering job generates many intermediate tables and sometimes causes space pressure in TEMP and UNDO, impacting OLTP users. Which action is the best practice to protect OLTP workload while still allowing the job to run?

Your organization must enforce that embeddings derived from sensitive text cannot be accessed by users outside a specific compliance role. The embeddings are stored in a shared table used by multiple applications. What is the best control to implement?

A recommendation system stores 1 billion item embeddings. Users need near-real-time similarity search with additional filters (region, availability) and predictable latency. Which architecture choice is most appropriate?

During an internal audit, you must prove that only approved model versions were used for scoring customer credit risk, and that scores can be traced back to the model and feature set. Which database-oriented design best satisfies this requirement?

An application stores both a document's text and its embedding in an Oracle database. During a similarity search, the results include many unrelated documents. Which is the most likely root cause to validate first?

You need to support a RAG (retrieval-augmented generation) workflow where users ask questions and the system retrieves the most semantically similar passages. Which data design best supports efficient retrieval and traceability back to the original source?

A data scientist wants to run model inference from within the database so that predictions are executed close to the data with consistent security controls and auditing. Which approach best meets this goal?

After implementing vector search, latency is inconsistent: some queries are fast while others are very slow even with similar TOP-K requests. Which administrative action is most appropriate to diagnose whether the issue is due to plan variability versus vector computation cost?

You must ensure that similarity search results only return documents a user is authorized to see, even though vectors are stored in a shared table. What is the best database-centric control to enforce this consistently for all queries?

A team complains that embedding generation and ingestion jobs are impacting OLTP response times. You want to reduce interference while keeping ingestion in the same database. Which strategy is most appropriate?

Your organization requires that any AI-generated summary stored back into the database must be traceable to the input records and the model prompt used. Which design best supports auditability and governance?

A vector search query is correct functionally, but performance degrades significantly as the vector table grows. You suspect unnecessary distance computations. Which query pattern is most likely to reduce work while keeping relevance high?

You manage a multi-tenant database where each tenant can bring its own embedding model. A security review flags a risk that tenants could infer information about other tenants via shared vector search behavior. Which control most directly mitigates this risk at the database layer?

A production pipeline updates embeddings when source text changes. After a deployment, similarity results drift and A/B tests show inconsistent relevance. There is no data loss. What is the most robust operational control to prevent silent embedding/model drift going forward?

You are onboarding a team to AI Vector Search. They created a table containing embeddings and a text column, then ran similarity queries. Performance is poor and full table scans occur. What is the recommended database object to create to improve similarity search performance?

A data science team wants to train an in-database machine learning model without exporting data to an external notebook environment. Which approach best meets the requirement while minimizing data movement?

A developer stored embeddings as a character string to quickly prototype. Similarity queries now fail or produce inconsistent results due to type conversions. What is the best practice to store embeddings for reliable vector operations?

Your organization must ensure that only an approved service account can call a database function that generates embeddings, and all calls must be auditable. Which combination best satisfies both least privilege and traceability?

A retrieval-augmented generation (RAG) workload stores document embeddings and uses top-k similarity search with a metadata filter (for example, document_type and region). Queries are slow because the filter is applied after vector similarity evaluation. What design change most directly improves performance while keeping results correct?

An in-database ML classification model’s accuracy dropped after a new data feed was introduced. Investigation shows new category values appear in several predictor columns. What is the most appropriate corrective action to restore model quality?

A DBA is asked to implement governance so that embeddings derived from regulated text (PII) cannot be queried by general analysts, while still allowing analysts to query non-regulated embeddings in the same table. Which approach best enforces this at the database layer?

After enabling vector search for a large table, users report inconsistent nearest-neighbor results between two environments that contain the same data. Both use approximate search. What is the most likely cause and best remediation to ensure deterministic, repeatable results for validation tests?

A team uses an external embedding service from inside the database through a network call. They must ensure the database cannot exfiltrate data to arbitrary endpoints, and the allowed endpoint list must be centrally managed. Which control best addresses this requirement?

Your database supports both operational transactions and vector search. During peak hours, vector similarity queries cause unpredictable latency for OLTP sessions. Which architecture pattern best isolates OLTP performance while still supporting near-real-time vector search?

You want to enable semantic search over product descriptions stored in an Oracle Database table. What is the best first step to make vector search possible on the text column?

A developer reports that their vector similarity query is returning results, but performance is slow and the plan shows a full scan of the embeddings table. Which action most directly addresses the issue?

You are designing an ingestion pipeline that periodically re-embeds documents when the source text changes. What is the best practice to keep vector search results consistent with the source data?

You have a table with a vector column storing embeddings and a separate JSON column with metadata. Users want to filter results by metadata (for example, category) and then perform vector similarity. What design best supports this requirement?

A data science team wants to run model scoring inside the database to reduce data movement and simplify governance. Which approach best aligns with this goal?

A team integrates a hosted embedding service with Oracle Database. Occasionally, the service times out and the ingestion job fails midway, leaving partial data. What is the best database-side strategy to make ingestion resilient and consistent?

After enabling a new AI-driven workload, you notice increased CPU usage and longer OLTP response times. You suspect resource contention between AI vector searches and critical transactions. What is the best administrative approach?

Your organization must prevent a human operator from viewing sensitive text used to generate embeddings while still allowing them to run similarity searches. Which control most directly supports this requirement?

A compliance audit requires you to prove who accessed sensitive AI-related tables and what queries were executed, including vector search queries. What is the best solution?

You run an internal retrieval-augmented generation (RAG) system. After re-embedding your entire corpus with a new embedding model, users report that similarity quality has degraded and some results look unrelated. Which root cause is most likely, and what is the best fix?

You are enabling AI Vector Search for a product-catalog application. Users complain that semantically unrelated items are returned because the query matches on common boilerplate text (for example, shipping and warranty paragraphs). Which approach is the BEST practice to improve result relevance without changing the application logic?

You are asked to allow data scientists to run in-database model training, but you must prevent them from reading raw customer PII. Which control is MOST appropriate to meet this requirement?

A retrieval-augmented generation (RAG) workflow stores embeddings in the database. After a bulk load, vector search latency becomes inconsistent. You suspect the index is not being used. What is the FIRST step you should take to confirm index usage at the database level?

You manage an Oracle-based vector store for support articles. Articles are continuously updated, and you want vector search to reflect changes quickly while keeping query performance stable. Which design is MOST appropriate?

A team integrates an external embedding service. They observe that identical input text sometimes produces different vectors over time, causing duplicate detection and similarity thresholds to fail. What is the BEST administrative recommendation to stabilize this pipeline?

Your organization requires that any LLM-generated text saved back into the database be traceable to the prompt, retrieval set, and model used. Which governance pattern BEST satisfies this requirement?

A vector search query is slow only for one application user, while it is fast for others running the same SQL. The database shows multiple child cursors for the statement. What is the MOST likely cause and best next action?

You are designing access control for a shared vector store used by multiple business units. Each unit must only retrieve vectors derived from its own documents, even if users can run ad hoc similarity searches. What is the MOST robust database-level approach?

You must choose a similarity metric and indexing strategy for a vector store where embeddings are produced by a model known to output vectors with variable magnitude. You want ranking to be based on direction (semantic similarity) rather than length. Which choice is MOST appropriate?

A regulated environment requires that prompts sent to an external LLM never include restricted data (for example, certain identifiers). Users can submit free-form prompts, and the RAG layer may retrieve sensitive snippets. Which database-centric control BEST reduces the risk of restricted data leaving the environment?