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Oracle AI Vector Search Professional Sample Questions (Q53-Q58):

NEW QUESTION # 53
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

A. To automatically optimize database queries for improved performance
B. To provide real-time monitoring and diagnostics for AI applications
C. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing

Answer: C

Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.

NEW QUESTION # 54
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

A. Perform the similarity search without a WHERE clause
B. Apply relational filters and a similarity search in the query
C. Use VECTOR_INDEX_HINT and NO WHERE clause
D. Use UNION ALL with vector operations

Answer: B

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.

NEW QUESTION # 55
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

A. A secure VPN tunnel
B. A new user account with elevated privileges
C. A dedicated OCI compartment
D. An AI profile for OCI Generative AI

Answer: D

Explanation:
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.

NEW QUESTION # 56
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A. Exact similarity search with a high target accuracy setting
B. Approximate similarity search with a low target accuracy setting
C. Relational filtering combined with an exact search
D. Exact similarity search using flat search

Answer: B

Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 57
Why would you choose to NOT define a specific size for the VECTOR column during development?

A. It restricts the database to a single embedding model
B. It limits the length of text that can be vectorized
C. It impacts the accuracy of similarity searches
D. Different external embedding models produce vectors with varying dimensions and data types

Answer: D

Explanation:
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.

NEW QUESTION # 58
......

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Sam Walker Sam Walker

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