Free Practice Questions for Oracle 1z0-184-25 Exam

Oracle Database 23ai imposes restrictions on DDL operations for tables with VECTOR columns to preserve data integrity. CTAS (A) is permitted, as it copies the VECTOR column intact into a new table, maintaining its structure. Dropping a VECTOR column (B) is allowed via ALTER TABLE DROP COLUMN, as it simply removes the column without altering its type. Adding a new VECTOR column (D) is supported with ALTER TABLE ADD, enabling schema evolution. However, modifying an existing VECTOR column’s data type to a non-VECTOR type (C) (e.g., VARCHAR2, NUMBER) is not permitted because VECTOR is a specialized type with dimensional and format constraints (e.g., FLOAT32), and Oracle does not support direct type conversion due to potential loss of semantic meaning and structure. This restriction is documented in Oracle’s SQL reference.

The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It’s designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.

An embedding model in the context of vector search, such as those used in Oracle Database 23ai, is fundamentally a machine learning construct (e.g., BERT, SentenceTransformer, or an ONNX model) designed to transform raw data—typically text, but also images or other modalities—into numerical vector representations (C). These vectors, stored in the VECTOR data type, encapsulate semantic meaning in a high-dimensional space where proximity reflects similarity. For instance, the word "cat" might be mapped to a 512-dimensional vector like [0.12, -0.34, …], where its position relative to "dog" indicates relatedness. This transformation is the linchpin of vector search, enabling mathematical operations like cosine distance to find similar items.

Option A (defining schema) misattributes a database design role to the model; schema is set by DDL (e.g., CREATE TABLE with VECTOR). Option B (executing searches) confuses the model with database functions like VECTOR_DISTANCE, which use the embeddings, not create them. Option D (storing vectors) pertains to the database’s storage engine, not the model’s function—storage is handled by Oracle’s VECTOR type and indexes (e.g., HNSW). The embedding model’s role is purely generative, not operational or structural. In practice, Oracle 23ai integrates this via VECTOR_EMBEDDING, which calls the model to produce vectors, underscoring its transformative purpose. Misunderstanding this could lead to conflating data preparation with query execution, a common pitfall for beginners.

In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle’s documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.

Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences—not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean’s magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean’s prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle’s vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question’s phrasing.

The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude—fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai’s FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle’s choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity—vital for users assuming defaults in queries.

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.

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.

IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn’t directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle’s IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.