Important Oracle 1Z0-184-25 Exam Questions

Answer: B

Explanation:

To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name 'v', the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.

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.

Answer: A

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.

Answer: D

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.

Answer: A

Explanation:

Oracle GoldenGate 23ai is a real-time data replication and integration tool, extended in 23ai to handle the VECTOR data type for AI applications. Its key advantage (A) is enabling real-time updates of vector data across distributed locations---e.g., replicating VECTOR columns from a primary database in New York to a secondary in London with sub-second latency. This ensures AI models (e.g., for similarity search or RAG) access the latest embeddings as source data (e.g., documents) changes, critical for dynamic environments like customer support systems where new queries demand current context. Imagine a VECTOR column storing embeddings of support tickets; GoldenGate keeps these synchronized across regions, minimizing staleness that could degrade AI responses.

Option B (automatic translation) is fictional; GoldenGate doesn't convert vector formats (e.g., FLOAT32 to INT8)---that's a model or application task. Option C (compression) isn't a GoldenGate feature; compression might occur at the storage layer, but GoldenGate focuses on replication fidelity, not size reduction. Option D (version control) misaligns with GoldenGate's purpose; it ensures data consistency, not historical versioning like Git. Real-time replication (A) stands out, as Oracle's documentation emphasizes GoldenGate's role in keeping vector-driven AI applications globally consistent, a game-changer for distributed AI deployments where latency or inconsistency could disrupt user trust. Without this, static exports (e.g., Data Pump) would lag, undermining real-time AI use cases.