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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

Topic 2

Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

Topic 3

Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.

Oracle AI Vector Search Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
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. Different external embedding models produce vectors with varying dimensions and data types
C. It impacts the accuracy of similarity searches
D. It limits the length of text that can be vectorized

Answer: B

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 # 53
Which operation is NOT permitted on tables containing VECTOR columns?

A. UPDATE
B. DELETE
C. SELECT
D. JOIN ON VECTOR columns

Answer: D

Explanation:
In Oracle 23ai, tables with VECTOR columns support standard DML operations: SELECT (A) retrieves data, UPDATE (B) modifies rows, and DELETE (C) removes rows. However, JOIN ON VECTOR columns (D) is not permitted because VECTOR isn't a relational type for equality comparison; it's for similarity search (e.g., via VECTOR_DISTANCE). Joins must use non-VECTOR columns. Oracle's SQL reference restricts VECTOR to specific operations, excluding direct joins.

NEW QUESTION # 54
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

A. Clusters similar text chunks and randomly selects one from the most relevant cluster
B. Converts the question to keywords, searches for matches, and inserts the text into the response
C. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
D. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM

Answer: C

Explanation:
In Oracle 23ai's RAG framework, vector similarity search (A) encodes a user question and database chunks into vectors (e.g., via VECTOR_EMBEDDING), computes similarity (e.g., cosine via VECTOR_DISTANCE), and retrieves the most relevant chunks. These are then included in the LLM prompt, augmenting its response with context. Training a separate LLM (B) is not RAG; RAG uses existing models. Keyword search (C) is traditional, not vector-based, and less semantic. Clustering and random selection (D) lacks precision and isn't RAG's approach. Oracle's documentation describes this encode-search-augment process as RAG's core mechanism.

NEW QUESTION # 55
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

A. Inverted File (IVF) index
B. Hierarchical Navigable Small World (HNSW) index
C. Binary Tree (BT) index
D. Inverted File System (IFS) index

Answer: B

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.

NEW QUESTION # 56
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

A. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
B. By requiring users to manually enter their OCI API keys each time they execute a natural language query
C. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
D. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service

Answer: A

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.

NEW QUESTION # 57
......

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Ian Green Ian Green

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