BTW, DOWNLOAD part of BraindumpsPass DP-800 dumps from Cloud Storage: https://drive.google.com/open?id=1ZIxjX0oEUmkpyg96BzKmcYV-g8dqMFNF
It is not a time to get scared of taking any difficult certification exam such as DP-800. The excellent study guides, practice questions and answers and dumps offered by BraindumpsPass are your real strength to take the test with confidence and pass it without facing any difficulty. Passing an DP-800 exam rewards you in the form of best career opportunities. A profile rich with relevant credentials opens up a number of career slots in major enterprises. BraindumpsPass's DP-800 Questions and answers based study material guarantees you career heights by helping you pass as many exams as you want.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Exam Format: | Case studies, Multiple choice, Build list, Multiple select, Drag and drop |
| Exam Price: | $165 USD |
| Passing Score: | 700 |
| Available Languages: | Chinese (Simplified), Chinese (Traditional), Japanese, Indonesian (Indonesia), English, Korean, Arabic (Saudi Arabia), Italian, German, Spanish, French, Portuguese (Brazil) |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: SQL AI Developer Associate |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or in-person at a testing center |
| Pre Condition: | No specific prerequisites, but candidates should have experience with SQL and a basic understanding of Azure services. Familiarity with AI or machine learning concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-800/ |
We are in a constant state of learning new knowledge, but also a process of constantly forgotten, we always learned then forget, how to solve this problem, the answer is to have a good memory method, our DP-800 study materials will do well on this point. Our DP-800 Study Materials have their own unique learning method, abandon the traditional rote learning, adopt diversified memory patterns, such as the combination of text and graphics memory method, to distinguish between the memory of knowledge.
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
NEW QUESTION # 20
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.
Answer:
Explanation:
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.
NEW QUESTION # 21
Hotspot Question
You have a database named db1. The schema is stored in a Git repository as an SDK-style SQL database project. The repository contains the following GitHub Action workflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 22
You have a SQL database in Microsoft Fabric that contains a table named WebSite. Logs. WebSite.Logs stores application telemetry data. Website.Logs contains a nvarehar(iMx) column named log that stores JSON documents You have a daily report that filters by the $.severity JSON property and returns Logld. LogDateTime, and log.
The report frequently causes full table scans.
You need to modify Website. Logs to support efficient filtering by $. severity and avoid key lookups for the columns returned by the report.
How should you complete the Transact-SQL code to avoid full table scans? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct way to avoid full table scans here is to add a computed column that extracts the JSON scalar property with JSON_VALUE , and then create a nonclustered index on that computed column with the report's returned columns in the INCLUDE list. Microsoft's JSON indexing guidance specifically recommends creating a computed column that exposes the JSON property you filter on, using the same expression as in the query, and then indexing that computed column.
So the computed column must be:
AS JSON_VALUE([log], ' $.severity ' ) PERSISTED
This is correct because $.severity is a scalar JSON value, so JSON_VALUE is the proper function.
JSON_QUERY would be for extracting an object or array, not a scalar property. Microsoft also notes that persisted computed columns can improve access speed for JSON-derived values.
The index should then include:
INCLUDE (LogId, LogDateTime, [log])
That is the right covering strategy because the report filters by severity but returns LogId, LogDateTime, and log. Microsoft's guidance on included columns explains that nonkey included columns let a nonclustered index cover more queries and reduce extra lookups to the base table.
So the completed code is:
ALTER TABLE WebSite.Logs
ADD severity AS JSON_VALUE([log], ' $.severity ' ) PERSISTED;
GO
CREATE INDEX ix_severity
ON WebSite.Logs(severity)
INCLUDE (LogId, LogDateTime, [log]);
GO
NEW QUESTION # 23
You have a Microsoft SQL Server 2025 instance that has a managed identity enabled.
You have a database that contains a table named dbo.ManualChunks. dbo.ManualChunks contains product manuals.
A retrieval query already returns the top five matching chunks as nvarchar(max)text.
You need to call an Azure OpenAI REST endpoint for chat completions. The solution must provide the highest level of security.
You write the following Transact-SQL code.
What should you insert at line 02?
Answer: C
Explanation:
To use a Managed Identity with sys.sp_invoke_external_rest_endpoint in SQL Server, you need to configure your database-scoped credential with the following specific values:
Required Settings
WITH IDENTITY: Must be set to Managed Identity.
SECRET: Must be set to SECRET = N'{"resourceid":"<value>"}'.
Example Statement
CREATE DATABASE SCOPED CREDENTIAL [MyManagedIdentityCredential]
WITH IDENTITY = 'Managed Identity',
SECRET = N'{"resourceid": "https://azure.com"}'; -- Or your specific resource URI Quick Breakdown Identity: Managed Identity tells SQL Server to use the instance's assigned Azure identity rather than a static key or token.
Secret: The resourceid inside the JSON string specifies the Audience (resource) for which the Managed Identity is requesting an access token (e.g., Azure Storage, Key Vault, or a custom API).
Reference:
https://learn.microsoft.com/en-us/azure/azure-sql/managed-instance/authentication-azure-ad- user-assigned-managed-identity-create-managed-instance
NEW QUESTION # 24
You have an Azure SQL database named SalesDB
You have a Data API builder (DAB) instance that exposes the following entities in SalesDB
* A table entity named Order mapped to a table named dbo. Orders
* A stored procedure entity named FinalizeOrder mapped to a stored procedure named dbo.usp_FinalizeOrder The DAB runtime configuration includes the following permissions.
Client requests include a Microsoft Entra access token. The client also sends HTTP header x-MS-APl-ROlE:
operations for both REST and GraphQL requests.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* A REST GET request to the order entity that includes the access token and x-MS-API-ROLE:
operations will return data. # No
* When DAB runs the stored procedure, the database policy defined on the FinalizeOrder entity will be enforced. # Yes
* If the client omits the x-MS-API-ROLE header but still sends the same access token, the order entity read request will run in the authenticated role context. # Yes The first statement is No . In Data API builder, when a valid token is sent with X-MS-API-ROLE, the request runs in that requested role if that role is present in the token . Here, that means the effective role becomes operations , not authenticated. But the order entity grants read only to the authenticated role, not to operations, so the GET request would not be authorized to return data.
The second statement is Yes . DAB evaluates the request against the permissions and policies configured for the effective role on the requested entity. The FinalizeOrder entity grants execute to role operations and includes a database policy of TenantId = @claims.tenantid, so that policy is part of the enforced authorization
/filtering behavior when the stored procedure entity is executed.
The third statement is Yes . If the client sends a valid access token without X-MS-API-ROLE, DAB uses the built-in Authenticated system role by default. Since the order entity allows read for the authenticated role, that read request runs in the authenticated role context.
Top of Form
Bottom of Form
NEW QUESTION # 25
......
DP-800 Most Reliable Questions: https://www.braindumpspass.com/Microsoft/DP-800-practice-exam-dumps.html
DOWNLOAD the newest BraindumpsPass DP-800 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1ZIxjX0oEUmkpyg96BzKmcYV-g8dqMFNF