Online Access Free DP-800 Exam Questions

You have an Azure SQL database that contains a table named knowledgebase, knowledgebase stores human resources (HR) policy documents and contains columns named title, content, category, and embedding.
You have an application named App1. App1 queries two relational tables named employee_pnofiles and benefits_enrollnent that contain HR data. App1 hosts a chatbot that calls a large language model (LLM) directly.
Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change. The chatbot also fails to answer questions that reference internal policy documents by title or category.
You need to recommend a Retrieval Augmented Generation (RAG) solution to resolve the chatbot issues.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

The correct recommendation is to retrieve grounding data from knowledge_base and, at inference time, generate query embeddings and run a vector similarity search .
The chatbot currently answers some general HR questions but fails when policies change and when users ask about internal policy documents by title or category . That is exactly the kind of problem RAG is meant to solve: ground the LLM in the organization's proprietary content instead of relying on the model's training data or unrelated transactional tables. Microsoft's RAG guidance states that RAG extends LLMs by grounding responses in your own content and that, for agentic retrieval, knowledge bases unify knowledge sources for retrieval.
So the grounding data should come from knowledge_base , because that table stores the HR policy documents and already includes fields like title, content, category, and embedding. Those are the fields directly tied to the missing and outdated policy answers. By contrast:
* employee_profiles and benefits_enrollment are operational HR tables, not the authoritative store for policy-document grounding.
* PDF exports of the policies would be inferior to querying the indexed/structured knowledge base already prepared for retrieval.
* The LLM training data is specifically the wrong source when the issue is outdated internal content.
For the retrieval step, Microsoft's guidance says to use embeddings for vector queries and notes that vector similarity search matches concepts, not exact terms . This is especially important because users ask about policy documents by title or category and also phrase questions in ways that might not exactly match document wording. Generating a query embedding and then running a vector similarity search is the appropriate retrieval step in a RAG pipeline.

You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
Create a vector index on the appropriate column.
Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
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 .

You have a SQL database in Microsoft Fabric that uses the default settings for a newly created database and contains a table named Sales.Orders.
You have an application that uses two stored procedures to access Sales.Orders.
While monitoring database activity, you discover the following:
While monitoring database activity, you discover the following:
* sys.dm_exec_requests shows multiple sessions in a suspended state with wait_type = LCK_M_X. All the sessions show the same wait_resource, which maps to Sales.Orders, and the same nonzero blocking_session_id.
* sys.dm_exec_input_buffer(blocking_session_id, NULL) returns a last submitted command of BEGIN TRANSACTION UPDATE Sales.Orders.
* sys.dm_exec_sessions for the blocking session shows status = sleeping and open_transaction_count = 1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
* The blocking is caused by an uncommitted explicit transaction in the blocking session that is holding locks. # Yes
* While an UPDATE operation on Sales.Orders is occurring, SELECT statements will be blocked. # No
* Joining sys.dm_tran_locks to sys.dm_exec_requests will show which session holds locks involved in the blocking chain. # Yes Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first statement is Yes . The evidence is conclusive: the blocking session is sleeping , has open_transaction_count = 1 , and its last submitted command was BEGIN TRANSACTION UPDATE Sales.
Orders . Microsoft specifically identifies a sleeping session with an open transaction as a classic blocking condition: modifications retain their exclusive locks until the transaction is committed or rolled back . The LCK_M_X waits and identical nonzero blocking_session_id values confirm that other requests are waiting for incompatible exclusive locks.
The second statement is No because READ_COMMITTED_SNAPSHOT is ON by default for SQL database in Microsoft Fabric . Under the default READ COMMITTED isolation level, reads therefore use row versioning rather than shared locks. Consequently, a normal SELECT can read the appropriate committed row version while an UPDATE holds locks, so the writer does not ordinarily block the reader. Microsoft explicitly documents RCSI as ON by default in SQL database in Microsoft Fabric .
The third statement is Yes in the context of blocking diagnostics. sys.dm_tran_locks exposes lock ownership through fields such as request_session_id , request_mode , and request_status , while sys.dm_exec_requests exposes active request and blocking_session_id information. Correlating these DMVs allows you to associate locks with sessions participating in a blocking chain. Microsoft specifically recommends sys.dm_tran_locks for determining which locks are currently held and sys.dm_exec_requests for identifying blocked and blocking sessions.

You need to enable similarity search to provide the analysts with the ability to retrieve the most relevant health summary reports. The solution must minimize latency.
What should you include in the solution?

• A.a standard nonclustered index on the Fmbeddings (vector (1536)) column
• B.a full-text index on the Fmbeddings (vector (1536)) column
• C.a computed column that manually compares vector values
• D.a vector index on the Embedding* (vector (1536)) column

You need to recommend a solution that will resolve the ingestion pipeline failure issues. Which two actions should you recommend? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.Add a check constraint that validates the JSON structure.
• B.Enable snapshot isolation on the database.
• C.Create a unique index on a hash of the payload.
• D.Add foreign key constraints on the table.
• E.Use a trigger to automatically rewrite malformed JSON.