Oracle AI Vector Search Professional Advanced Practice Exam: Hard Questions 2025

An enterprise application stores 50 million high-dimensional vectors (1536 dimensions) and requires sub-50ms query latency at the 99th percentile while maintaining 95%+ recall. The system experiences a 10:1 read-to-write ratio with frequent bulk updates of 100K vectors every 6 hours. Which indexing strategy and configuration provides optimal performance?

During vector similarity search performance analysis, you observe that queries with VECTOR_DISTANCE(vector_column, :query_vector, COSINE) consistently perform 40% slower than equivalent VECTOR_DISTANCE(vector_column, :query_vector, DOT) operations, despite both vectors being L2-normalized. The execution plans show identical index access patterns. What is the most likely root cause and solution?

A RAG (Retrieval-Augmented Generation) system uses vector search to retrieve relevant document chunks before LLM processing. Users report that semantically relevant results are being excluded while less relevant results appear in the top-k. Vector distances for excluded results are only marginally higher (0.02-0.05 difference) than included results. The system uses HNSW index with default parameters and retrieves top-20 results. What is the most effective solution?

An application performs hybrid search combining traditional WHERE clause filters (category='electronics' AND price < 500) with vector similarity search on 100M records. Only 0.5% of records match the filter criteria. The query plan shows VECTOR_INDEX_SCAN followed by filter application, resulting in poor performance. Which optimization strategy provides the best performance improvement?

Your organization needs to implement multi-tenancy for vector search where 10,000 tenants each have 1K-100K vectors. Tenants require strict data isolation, independent index management, and the ability to scale individual tenant storage. Query patterns show 80% of requests target 5% of tenants (hot tenants). What architecture best addresses these requirements?

A vector search system experiences significant performance degradation after reaching 20M vectors. Analysis reveals that the HNSW index size has grown to 250GB with M=32, and query latency has increased from 15ms to 180ms. Memory-mapped I/O shows high page fault rates. The system has 128GB RAM and cannot be upgraded. Which approach most effectively addresses this issue?

A real-time recommendation system ingests 50K new product vectors per hour while serving 10K queries per second. The current implementation shows lock contention on the vector index during concurrent inserts and queries, causing query latency spikes up to 500ms during peak insert periods. What is the most effective solution to eliminate lock contention while maintaining data freshness?

During disaster recovery testing, you discover that vector index reconstruction after database restore takes 14 hours for 80M vectors, violating the 4-hour RTO requirement. The vectors themselves restore quickly from backups, but index rebuild is the bottleneck. Full index metadata is not included in regular backups. What strategy best achieves the RTO target?

A semantic search application uses multilingual embeddings (105 languages) where users can query in any language and retrieve results in any language. The system shows significantly degraded recall (65%) for cross-lingual queries (query in English, retrieve Spanish results) compared to monolingual queries (95% recall). Vector distances for cross-lingual relevant results are 0.15-0.25 higher than monolingual. How should you address this?

Your vector search system must comply with GDPR's right-to-erasure, requiring complete removal of a user's data within 72 hours. The challenge is that user-generated content vectors are used to train periodically updated embedding models, and user vectors may influence HNSW graph structures of other users' vectors through graph edges. What approach ensures compliant data deletion?