1Z0-184-25 OFFICIAL PRACTICE TEST | ONLINE 1Z0-184-25 VERSION

1Z0-184-25 Official Practice Test | Online 1Z0-184-25 Version

1Z0-184-25 Official Practice Test | Online 1Z0-184-25 Version

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100% Pass Oracle - 1Z0-184-25 - Authoritative Oracle AI Vector Search Professional Official Practice Test

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
Topic 2
  • 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.
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.
Topic 4
  • 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.

Oracle AI Vector Search Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

  • A. Use sparse format for vector data
  • B. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
  • C. Rely on SQL*Loader's automatic normalization of vector data
  • D. Enclose vector components in curly braces ({})

Answer: D

Explanation:
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.


NEW QUESTION # 39
If a query vector uses a different distance metric than the one used to create the index, whathappens?

  • A. An exact match search is triggered
  • B. A warning is logged, but the query executes
  • C. The index automatically updates
  • D. The query fails

Answer: D

Explanation:
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.


NEW QUESTION # 40
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 insert operation fails, and an error message is thrown
  • B. The database pads the vector with zeros to match the defined dimensions
  • C. The database ignores the defined dimensions and inserts the vector as is
  • D. The database truncates the vector to fit the defined dimensions

Answer: A

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 # 41
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

  • A. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))
  • B. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
  • C. UPDATE my_table SET v = VECTOR(4, FLOAT32)
  • D. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)

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.


NEW QUESTION # 42
Why would you choose to NOT define a specific size for the VECTOR column during development?

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

Answer: D

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 # 43
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