Oracle AI Database Administration Professional Intermediate Practice Exam: Medium Difficulty 2025

Your organization is implementing AI Vector Search for a semantic search application that handles product descriptions. The application requires similarity searches on embeddings with 1536 dimensions. You need to optimize query performance while maintaining acceptable accuracy. Which combination of index type and distance metric would be most appropriate for this use case?

A database administrator needs to configure vector search with partitioning for a table containing 50 million product vectors. The application requires fast similarity searches within specific product categories. Which partitioning strategy would best support this requirement while maintaining vector search performance?

Your team is integrating an external machine learning model that generates embeddings using Python. The model needs to process data stored in Oracle Database and return vector embeddings for storage. Which approach provides the most efficient and secure integration while minimizing data movement?

A financial services company needs to implement vector similarity search for fraud detection while ensuring that sensitive customer data vectors are protected. The system must support similarity searches while maintaining compliance with data privacy regulations. What combination of features should be implemented?

Your database supports an AI application that performs vector similarity searches. You notice that query performance has degraded over time as the vector table has grown to 10 million rows. The VECTOR index exists but queries are slower than expected. What diagnostic steps and actions should you take to optimize performance?

An e-commerce platform needs to implement a recommendation engine using vector similarity search. The application requires searching for the top 10 most similar products from a catalog of 5 million items, with response times under 100ms. Which configuration approach would best meet these requirements?

Your organization is deploying machine learning models that need to score data stored in the database. The models were trained externally using TensorFlow. You need to operationalize these models for real-time inference within database queries. Which approach should you implement?

A healthcare application stores patient medical record embeddings for similarity-based case matching. Regulations require comprehensive audit trails showing who accessed which patient vectors and when. Additionally, certain user roles should only access vectors for patients in their department. How should you configure the database to meet these requirements?

Your database hosts an AI application that performs both vector similarity searches and traditional OLTP transactions. You notice resource contention between these workloads. The vector searches are resource-intensive but can tolerate slightly higher latency than OLTP operations. How should you manage these competing workloads?

A data science team wants to develop and train machine learning models using data in Oracle Database without moving data to external systems. The models need to handle both structured data from business tables and vector embeddings. The team is proficient in Python and SQL. Which Oracle technology stack would best support their requirements?