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

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

To speed up repeated aggregate retrieval without changing base-table structure, the right pattern is an indexed view . Microsoft requires that an indexed view be created with WITH SCHEMABINDING , and if the view uses GROUP BY , it must also include COUNT_BIG(*) . After that, the first index on the view must be a unique clustered index .

The other options fail Microsoft’s indexed-view rules:

A is invalid because ORDER BY is not allowed in the indexed-view definition.

B is invalid because indexed views require WITH SCHEMABINDING , and the first index cannot just be a nonclustered index.

C is incomplete because a grouped indexed view must include COUNT_BIG(*) .

The correct column to use for generating embeddings is incidentDescrlption because embeddings are intended to represent the semantic meaning of rich textual content , not simple categorical, numeric, or location-only values. Microsoft’s DP-800 study guide explicitly includes skills such as identifying which columns to include in embeddings , generating embeddings , and implementing semantic vector search for scenarios where users need to find similar records based on meaning rather than exact matches.

In this scenario, analysts report that it is difficult to find similar incidents based on details such as weather, traffic conditions, and location . Those are descriptive context elements that are typically captured in a free-text incident description field. An embedding generated from incidentDescrlption can encode the semantic relationships among these narrative details, making it suitable for similarity search , semantic search , and RAG retrieval . Microsoft documentation on vectors and embeddings explains that embeddings are generated from text data and then stored for vector search to find semantically related items.

The other options are weaker choices:

vehicleLocation is too narrow and usually better handled with geospatial filtering , not embeddings.

incidentType is likely categorical and too low in semantic richness.

SeverityScore is numeric and not appropriate as the primary source for semantic embeddings.

Microsoft also notes that when multiple useful attributes exist, you can either embed each text column separately or concatenate relevant text fields into one textual representation before generating the embedding. But among the options given, the best and most exam-aligned answer is the textual narrative column : incidentDescrlption .