DP-800 Exam Dumps : Developing AI-Enabled Database Solutions

The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext , which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.

That makes randomized encryption the right choice here:

It protects data at rest and in transit/query parameters under Always Encrypted’s client-side encryption model.

It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.

Why the other options are wrong:

A. Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption .

C. RLS controls row access, not value confidentiality.

D. Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.

The correct answer is D because the requirement is to enable similarity search over embedding vectors and to minimize latency . Microsoft documents that CREATE VECTOR INDEX is specifically used to create an index on vector data for approximate nearest neighbor (ANN) search , which is designed to accelerate vector similarity queries compared to exact k-nearest-neighbor scans.

This matches the scenario exactly. The VehicleHealthSummary table already includes an Embeddings (vector(1536)) column. In Microsoft SQL platforms, embeddings are stored in vector columns and queried for semantic similarity. To improve performance and reduce response time, Microsoft recommends a vector index , not a regular B-tree nonclustered index and not a full-text index. A vector index is purpose-built for finding the most similar vectors efficiently.

The other options are not appropriate:

A would require manual comparison logic and would increase latency rather than minimize it.

B is incorrect because a standard nonclustered index is not the index type used for vector similarity operations.

C is incorrect because full-text indexes are for textual token-based search, not numeric vector embeddings.

Microsoft’s current documentation is explicit that vector indexes support approximate nearest neighbor search , and that the optimizer can use the ANN index automatically for vector queries. That is the exam-aligned design choice when the goal is fast retrieval of the most relevant health summary reports from an embeddings column.

The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads , and these two controls address those two problems directly. Microsoft’s JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON , and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.

For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design. Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index . That makes a persisted hash-based computed column plus a unique index a practical and exam-consistent way to reject duplicate payloads efficiently.

The other options do not solve the stated root causes:

Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.

A trigger to rewrite malformed JSON is not the right integrity control and is brittle.

Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention