DP-800 Certification Exam Guide + Practice Questions Updated 2026

Explanation:
Azure Functions’ Azure SQL trigger requires change tracking to be enabled on the source table. Microsoft’s SQL trigger documentation states that setting up change tracking for the Azure SQL trigger requires two steps: enable change tracking on the database and enable change tracking on the table being monitored. Since the question asks specifically which database configurations you should perform, enabling change tracking on dbo.Employee is one of the required database-side steps.
To meet the latency requirement that changes trigger the function within five seconds, the relevant trigger setting is Sql_Trigger_PollingIntervalMs. Microsoft documents this setting as the delay, in milliseconds, between processing each batch of changes, and a value of 5000 means the trigger polls every 5 seconds.
A few clarifications about the other options:
B is not the documented setting name. The documented app setting is Sql_Trigger_BatchSize or host setting MaxBatchSize, not “SetSql Trigger MaxBatchSize”. The screenshot wording suggests a distractor.
D is also required in practice for the trigger to work, but the question asks for two answers and includes the polling setting plus the table-level CT setting as the actionable choices presented.
F is wrong because the Azure SQL trigger uses change tracking, not CDC.

Explanation:
Yes, this meets the goal. Microsoft documents that on a partitioned table, you can use TRUNCATE TABLE ... WITH (PARTITIONS (...)) to remove data from a specific partition, and that this is an efficient maintenance operation that targets only that data subset rather than the whole table. Microsoft’s partitioning guidance explicitly lists truncating a single partition as an example of a fast partition-level maintenance or retention operation.
That matches the requirement to remove the oldest month while minimizing impact on other queries. Because the table is already partitioned by month on OrderDate, identifying the partition number for that oldest month and truncating only that partition is the correct low-impact approach, assuming the table and indexes are aligned as required for partition truncation.

Explanation:
The table definition allows Owner to be nullable because it was created as Owner nvarchar(100) without NOT NULL. Since the question asks what to add so that future rows must have a value, a check constraint such as CHECK (Owner IS NOT NULL) is the appropriate choice. Microsoft documents that check constraints validate future INSERT and UPDATE operations against the constraint condition.
The other options do not solve the requirement:
A foreign key enforces referential integrity, not non-null entry by itself. A nonclustered index does not require values to be present.
A unique constraint prevents duplicate values but still does not serve as the right mechanism here for enforcing presence across future writes. Microsoft’s constraint documentation also notes that primary-key columns are implicitly NOT NULL, which helps distinguish nullability enforcement from other constraint types.

Explanation:
The correct answer is B because the problem is not the vector search operator itself. The problem is that embeddings are becoming stale when article content changes. Microsoft documents that change data capture (CDC) tracks insert, update, and delete operations on source tables, which makes it the right mechanism to identify only the rows that changed.
This also best satisfies the requirement to minimize CPU usage on SalesDB. With CDC, the database only records the row changes, and the embedding regeneration work can be moved to an external process such as an Azure Functions app. That avoids running embedding generation inline inside the database for every update and avoids repeatedly recalculating embeddings for unchanged rows. In contrast, an hourly full-table regeneration would be extremely wasteful on a table with two million frequently updated articles, and a trigger that calls embedding generation per row would push expensive AI work into the transactional path of the database.
Option A is incorrect because changing from VECTOR_SEARCH to VECTOR_DISTANCE does not regenerate embeddings; it only changes the retrieval method. Microsoft states that VECTOR_SEARCH is the ANN search function, while VECTOR_DISTANCE performs exact distance calculation, so neither option addresses stale embedding data.
So the right design is:
use CDC to detect only changed articles,
process those changes outside the database,
regenerate embeddings only for changed rows,
write back the refreshed embeddings for current semantic search results.

Explanation:
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.