NEW ORACLE 1Z0-184-25 PRACTICE TEST - GET READY WITH 1Z0-184-25 EXAM DUMPS [2025]

New Oracle 1Z0-184-25 Practice Test - Get Ready With 1Z0-184-25 Exam Dumps [2025]

New Oracle 1Z0-184-25 Practice Test - Get Ready With 1Z0-184-25 Exam Dumps [2025]

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Oracle AI Vector Search Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?

  • A. The database ignores the defined dimensions and inserts the vector as is
  • B. The database pads the vector with zeros to match the defined dimensions
  • C. The insert operation fails, and an error message is thrown
  • D. The database truncates the vector to fit the defined dimensions

Answer: C

Explanation:
In Oracle Database 23ai, a VECTOR column with a defined dimension count (e.g., VECTOR(4, FLOAT32)) enforces strict dimensional integrity to ensure consistency for similarity search and indexing. Attempting to insert a vector with a mismatched number of dimensions-say, TO_VECTOR('[1.2, 3.4, 5.6]') (3D) into a VECTOR(4)-results in the insert operation failing with an error (D), such as ORA-13199: "vector dimension mismatch." This rigidity protects downstream AI operations; a 3D vector in a 4D column would misalign with indexed data (e.g., HNSW graphs), breaking similarity calculations like cosine distance, which require uniform dimensionality.
Option A (truncation) is tempting but incorrect; Oracle doesn't silently truncate [1.2, 3.4, 5.6] to [1.2, 3.4]-this would discard data arbitrarily, risking semantic loss (e.g., a truncated sentence embedding losing meaning). Option B (padding with zeros) seems plausible-e.g., [1.2, 3.4, 5.6] becoming [1.2, 3.4, 5.6, 0]-but Oracle avoids implicit padding to prevent unintended semantic shifts (zero-padding could alter distances). Option C (ignoring dimensions) only applies to undefined VECTOR columns (e.g., VECTOR without size), not fixed ones; here, the constraint is enforced. The failure (D) forces developers to align data explicitly (e.g., regenerate embeddings), ensuring reliability-a strict but necessary design choice in Oracle's AI framework. In practice, this error prompts debugging upstream data pipelines, avoiding silent failures that could plague production AI systems.


NEW QUESTION # 37
You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

  • A. VECTOR_DISTANCE
  • B. FROM_VECTOR
  • C. TO_VECTOR
  • D. VECTOR_SERIALIZE

Answer: C

Explanation:
In Oracle Database 23ai, the TO_VECTOR function (A) converts a string representation of a vector (e.g., '[1.2, 3.4]') into a VECTOR data type with specified format (e.g., FLOAT32) and dimensions (here, 2). It's designed for creating vectors from text input, matching the requirement. VECTOR_DISTANCE (B) calculates distances between vectors, not generates them.FROM_VECTOR (C) isn't a documented function; it might be confused with serialization or extraction, but it's not standard. VECTOR_SERIALIZE (D) converts a vector to a string, the opposite of what's needed. Oracle's SQL reference confirms TO_VECTOR for this purpose, parsing the string into a 2D FLOAT32 vector.


NEW QUESTION # 38
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 establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
  • C. By requiring users to manually enter their OCI API keys each time they execute a natural language query
  • D. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols

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 # 39
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

  • A. It always guarantees 100% accuracy
  • B. It is slower than exact similarity search
  • C. It compares every vector in the dataset
  • D. It trades off accuracy for faster performance

Answer: D

Explanation:
Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.


NEW QUESTION # 40
Why would you choose to NOT define a specific size for the VECTOR column during development?

  • A. Different external embedding models produce vectors with varying dimensions and data types
  • B. It impacts the accuracy of similarity searches
  • C. It restricts the database to a single embedding model
  • D. It limits the length of text that can be vectorized

Answer: A

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 # 41
......

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