Explanation: Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps —like defining policies or configuring the Autonomous Database —depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle’s Select AI documentation lists compartment setup as the starting point in OCI configuration. Reference: Oracle Database 23ai New Features Guide, Section on Select AI Setup. How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?
Explanation: Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with GoldenGate (B) supports real -time vector data distribution. SQL*Loader (C) loads vector data into VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata’s storage optimizations. However, “Native Support for Vector Search Only within the Database Server” (A) is not a feature; vector search is natively supported across Exadata’s architecture, leveraging both database and storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents Exadata’s distributed capabilities, making it the correct “NOT” answer. Reference: Oracle Database 23ai Exadata System Software Guide, Chapter on AI Features. Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?
D. A warning is logged, but the query executes Explanation: In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine -based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index’s structure. An exact match search (B) doesn’t occur automatically; Oracle requires explicit control. The index doesn’t update itself (C), and warnings (D) are not the default behavior —errors are raised instead. Oracle’s documentation mandates metric consistency for index usage. Reference: Oracle Database 23ai AI Vector Search Guide, Section on Vector Index Metrics. What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?
connection = oracledb.connect(user=un, password=pw, dsn=ds) table_name = "Page" with connection.cursor() as cursor: create_table_sql = f""" CREATE TABLE IF NOT EXISTS {table_name} ( id NUMBER PRIMARY KEY, payload CLOB CHECK (payload IS JSON), vector VECTOR )""" try: cursor.execute(create_table_sql) except oracledb.DatabaseError as e: raise connection.autocommit = True from sentence_transformers import SentenceTransformer encoder = SentenceTransformer('all -MiniLM -L12-v2')
Explanation: In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high -dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn’t filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn’t convert vector formats (B); vectors are already in the VECTOR type. It also doesn’t specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle’s documentation confirms COSINE as one of the supported metrics for similarity search. Reference: Oracle Database 23ai SQL Language Reference, Section on VECTOR_DISTANCE. In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database? bash CollapseWrapCopy docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections] # Sample the resulting data docs[:2]
➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
