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Oracle 1Z0-184-25 Exam Syllabus Topics:
Topic
Details
Topic 1
- Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.
Topic 2
- 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 3
- Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
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Oracle AI Vector Search Professional Sample Questions (Q16-Q21):
NEW QUESTION # 16
What is the purpose of the Vector Pool in Oracle Database 23ai?
- A. To manage database partitioning
- B. To enable longer SQL execution
- C. To store non-vector data types
- D. To store HNSW vector indexes and IVF index metadata
Answer: D
Explanation:
The Vector Pool in Oracle 23ai is a dedicated SGA memory region (controlled by VECTOR_MEMORY_SIZE) for vector operations, specifically storing HNSW indexes (graph structures) and IVF index metadata (e.g., centroids) (B). This optimizes memory usage for vector search, keeping critical index data accessible for fast queries. Partitioning (A) is unrelated; that's a tablespace feature. Longer SQL execution (C) might benefit indirectly from memory efficiency, but it's not the purpose. Non-vector data (D) resides elsewhere (e.g., PGA, buffer cache). Oracle allocates the Vector Pool to enhance AI workloads, ensuring indexes don't compete with other memory, a design choice reflecting vector search's growing importance.
NEW QUESTION # 17
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?
- A. EFCONSTRUCTION
- B. NEIGHBOURS
- C. TARGET_ACCURACY
- D. VECTOR_MEMORY_SIZE
Answer: A
Explanation:
In Oracle 23ai, EFCONSTRUCTION (A) controls the number of closest vector candidates (edges) considered during HNSW index construction, affecting the graph's connectivity and search quality. Higher values improve accuracy but increase build time. VECTOR_MEMORY_SIZE (B) sets memory allocation, not candidate count. NEIGHBOURS (C) isn't a parameter; it might confuse with NEIGHBOR_PARTITIONS (IVF). TARGET_ACCURACY (D) adjusts query-time accuracy, not index creation. Oracle's HNSW documentation specifies EFCONSTRUCTION for this purpose.
NEW QUESTION # 18
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?
- A. It supports hierarchical partitioning of vectors
- B. It guarantees higher accuracy than Euclidean Distance
- C. It is simpler and faster because it avoids square-root calculations
- D. It is the default distance metric for Oracle AI Vector Search
Answer: C
Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.
NEW QUESTION # 19
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?
- A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
- B. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
- C. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
- D. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
Answer: A
Explanation:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.
NEW QUESTION # 20
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]
- A. It helps differentiate between chunks from different files but has no impact on vectorization
- B. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
- C. It speeds up the vectorization process by providing a unique identifier for each chunk
- D. It improves the accuracy of the LLM by providing additional training data
Answer: B
Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.
NEW QUESTION # 21
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