1Z0-184-25 Oracle AI Vector Search Professional Practice Questions
Prepare for 1Z0-184-25 with more than an answer.
- Exam fee
- $245 USD
- Time limit
- 90 minutes
- Questions on the exam
- 50
- Passing score
- 68%
- Level
- Professional
- Valid for
- Certification does not expire (Oracle certifications for Database 23ai family are not date-limited unless stated); verify current retirement status on Oracle CertView
Domains covered on the exam 6
- Understanding Vector Fundamentals20%
- Using Vector Indexes15%
- Performing Similarity Search15%
- Using Vector Embeddings15%
- Building a Retrieval Augmented Generation (RAG) solution25%
- Leveraging related AI capabilities10%
- 1
A database administrator is evaluating vector index types for a new AI application. The dataset consists of 50 million vectors, and the queries require sub-millisecond latency with the highest possible recall. The server has limited RAM (SGA/PGA) available.
Which statement accurately describes the architectural constraints of choosing an HNSW index in this scenario?
Show answer details
Correct answer: B
HNSW (Hierarchical Navigable Small World) indexes provide excellent latency and recall but maintain an in-memory graph architecture. For large datasets like 50 million vectors, this graph consumes significant memory (SGA/PGA). If RAM is limited, HNSW might cause memory exhaustion or heavy swapping, making IVF a better alternative despite HNSW's superior raw performance.
graph LR A[Query Vector] --> B[In-Memory Graph Layer 2] B --> C[In-Memory Graph Layer 1] C --> D[In-Memory Graph Layer 0] D --> E[Results] style B fill:#f9f,stroke:#333,stroke-width:2px style C fill:#f9f,stroke:#333,stroke-width:2px style D fill:#f9f,stroke:#333,stroke-width:2px - 2
An architect is tuning an IVF (Inverted File Flat) vector index to optimize the balance between search speed and recall accuracy. The underlying table has 10 million rows.
What is the primary operational effect of significantly increasing the number of neighbor partitions (centroids) during the IVF index creation?
Show answer details
Correct answer: B
In an IVF index, vectors are grouped into partitions (clusters) defined by centroids. Increasing the number of partitions means each partition holds fewer vectors. During a search, scanning a partition is faster. However, if the query only visits a fixed number of partitions, a highly partitioned space might cause the algorithm to miss relevant vectors in adjacent partitions, thereby lowering recall unless accuracy targets are tuned.
- 3
When creating a vector index in Oracle Database 23ai, the
TARGET ACCURACYparameter is often specified. What is the primary purpose of setting this parameter?Show answer details
Correct answer: B
The TARGET ACCURACY parameter informs the Oracle optimizer of the desired recall rate (e.g., 90%). The database uses this target to automatically tune internal search parameters (like how many graph nodes or IVF partitions to visit) to balance execution speed against the accuracy of the approximate similarity search.
- 4
Which TWO statements correctly describe the differences between HNSW and IVF vector indexes in Oracle Database 23ai? (Select TWO)
Show answer details
Correct answer: A, C
HNSW (Hierarchical Navigable Small World) builds a multi-layered graph that resides primarily in memory for ultra-fast traversal. IVF (Inverted File Flat) clusters vectors into partitions (centroids) and is designed to operate efficiently from disk, making it more memory-efficient for massive datasets.
Because HNSW maintains complex graph structures (nodes and edges across multiple layers) in memory to achieve high performance, it inherently consumes a significantly larger memory footprint compared to the partitioned, disk-friendly approach of IVF.
- 5
Oracle Database 23ai supports storing vector dimensions specifically in FLOAT32, FLOAT64, INT8, and BINARY formats to optimize storage and precision based on the embedding model used.
Show answer details
Correct answer: A
Oracle Database 23ai introduced the VECTOR data type, which natively supports FLOAT32, FLOAT64, INT8, and BINARY dimension formats. This flexibility allows database administrators to optimize for storage space and computational performance depending on the requirements of the specific embedding model.
- 6
A data engineer is designing a similarity search query for a product catalog. The embedding model used normalizes all vectors to a length of 1. Which vector distance metric should the engineer select to find the most semantically similar products while maintaining optimal computational efficiency?
Show answer details
Correct answer: C
For vectors that are normalized to a length of 1 (unit vectors), the Dot Product is mathematically proportional to Cosine similarity but is computationally much faster because it skips the magnitude division steps. Therefore, DOT PRODUCT is the most optimal metric for normalized vectors. Euclidean distance would work but is computationally heavier and typically used when magnitude matters.
