Microsoft DP-800 Exam Dumps - Actual Questions Answers

The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft’s SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.

The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.

Verified Answer : =

Vector index column → embedding

Vector search function → VECTOR_SEARCH

Final clause → ORDER BY s.distance

The first selection is embedding because CREATE VECTOR INDEX must target the column that stores vector data. Microsoft documents the syntax as CREATE VECTOR INDEX ... ON object (vector_column) , so the index belongs on dbo.Products.embedding , not on distance or product_name .

The second selection is VECTOR_SEARCH . This function is designed for vector similarity retrieval and works with vector indexes in SQL database in Microsoft Fabric . In the shown code, the supplied @query_vector VECTOR(1536) is compared against the embedding column using cosine distance, and TOP_N = 10 requests the ten nearest matches. Microsoft specifically documents VECTOR_SEARCH for approximate nearest-neighbor searches and its integration with vector indexes.

The final clause is ORDER BY s.distance because VECTOR_SEARCH returns a distance value representing how far each stored vector is from the supplied query vector. With cosine distance, smaller distance means greater similarity , so ascending order returns the most relevant products first.

Therefore, the completed selections are:

CREATE VECTOR INDEX idx_products_embedding

ON dbo.Products (embedding)

WITH (METRIC = ' cosine ' , TYPE = ' DiskANN ' );

FROM VECTOR_SEARCH (

TABLE = dbo.Products AS t,

COLUMN = embedding,

SIMILAR_TO = @query_vector,

METRIC = ' cosine ' ,

TOP_N = 10

) AS s

ORDER BY s.distance;

So the verified hotspot answers are embedding → VECTOR_SEARCH → ORDER BY s.distance .