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

JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText

CONTAINS(FeedbackJson, @Keyword)

SimilarityScore

These three selections are the correct way to complete the query because they align exactly with the stated requirements for the FeedbackJson column.

First, to extract the customer feedback text from the JSON document , the correct expression is JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText . Microsoft documents that JSON_VALUE is used to extract a scalar value from JSON, while JSON_QUERY is used for returning an object or array . Since $.text is the textual feedback string, JSON_VALUE is the correct function.

Second, to filter rows where the JSON text contains a keyword , the best choice is CONTAINS(FeedbackJson, @Keyword) . The scenario explicitly states that FeedbackJson already has a full-text index , and Microsoft documents that CONTAINS is the full-text predicate used in the WHERE clause to search full-text indexed character data. That makes it more appropriate than using EDIT_DISTANCE for keyword filtering.

Third, to order the results by similarity score, highest first , the correct item is SimilarityScore in the ORDER BY clause, which would be paired with DESC in the query. This matches the requirement to sort by the computed fuzzy similarity value. The DP-800 study guide specifically includes writing queries that use fuzzy string matching functions such as EDIT_DISTANCE, which supports the earlier computed SimilarityScore expression in the query.

When you change embedding models, the stored vectors should be treated as belonging to a different embedding space unless you intentionally keep the entire corpus consistent. Microsoft’s vector guidance notes that when most or all embeddings are replaced with fresh embeddings from a new model, the recommended practice is to reload the new embeddings and, for large-scale replacement scenarios, consider dropping and recreating the vector index afterward so search quality remains predictable.

This question also says applications must continue to use VECTOR_SEARCH without runtime errors . VECTOR_SEARCH requires compatible vector dimensions, and the vector column already exists. Azure OpenAI documentation shows that text-embedding-ada-002 is fixed at 1536 dimensions and text-embedding-3-small supports up to 1536 dimensions . That means the migration can remain compatible with a VECTOR(1536) column, but the right implementation step is still to re-embed the existing rows so the table does not contain a mixed corpus produced by different models.

The other options are not appropriate:

B normalization does not solve a model migration problem.

C converting the vector column to nvarchar(max) would break vector-native search design.

D a vector index improves performance, but it does not migrate old embeddings to the new model.

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(*) .