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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Real Exam Qty: | 40-60 |
| Exam Format: | Case studies, Multiple select, Multiple choice, Active screen, Drag and drop |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 (on a scale of 1-1000) |
| Available Languages: | Portuguese (Brazil), Korean, Spanish, English, French, Chinese (Simplified), Japanese, German |
| Recommended Training: | Microsoft Learn DP-800 Learning Path |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: T-SQL development, SQL Server/Azure SQL, CI/CD practices, basic AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800 |
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NEW QUESTION # 32
Which service enables natural language querying over SQL data?
Answer: A
Explanation:
Azure OpenAI Service allows users to query structured SQL data using natural language via LLMs.
NEW QUESTION # 33
You have an Azure SQL database that supports the OLTP workload of an order-processing application.
During a 10-minute incident window, you run a dynamic management view query and discover the following:
Session 72 is sleeping with open_transaction_count = 1.
Multiple other sessions show blocking_session_id = 72 in sys.dm_exec_requests.
sys.dm_exec_input_buffer(72, NULL) returns only BEGIN TRANSACTION UPDATE Sales.Orders.
Users report that updates to Sales.Orders intermittently time out during the incident window. The timeouts stop only after you manually terminate session 72.
What is a possible cause of the blocking?
Answer: C
Explanation:
The best explanation is an open explicit transaction . During the incident, session 72 was sleeping but still had open_transaction_count = 1 , and sys.dm_exec_input_buffer(72, NULL) showed only BEGIN TRANSACTION UPDATE Sales.Orders. That pattern indicates the session executed an update inside an explicit transaction and then remained idle without committing or rolling back , while still holding locks.
Other sessions showing blocking_session_id = 72 is the expected symptom of that situation. Microsoft explains that blocking occurs when one session holds a lock on a resource and another session requests a conflicting lock, and sleeping sessions can continue to block if they retain locks through an open transaction.
This also fits the observed behavior that the timeouts stopped only after session 72 was terminated . Killing the session would roll back the active transaction and release the locks, allowing waiting updates to continue.
That is much more consistent with an uncommitted transaction than with a deadlock, because deadlocks are normally detected and one session is chosen as the victim automatically rather than persisting until manual termination.
NEW QUESTION # 34
You have an Azure SQL table that contains the following data.
You need to retrieve data to be used as context for a large language model (LLM). The solution must minimize token usage.
Which formal should you use to send the data to the LLM?
Answer: B
Explanation:
The correct choice is Option A because it provides the relevant semantic context the LLM needs while avoiding an unnecessary field that would add tokens without improving answer quality.
For LLM grounding and RAG-style context, Microsoft guidance emphasizes mapping and sending the fields that contain text pertinent to the use case . In this FAQ scenario, the useful context is the ProductName , the Question , and the Answer . Those three fields help the model understand both the subject domain and the actual Q & A pair. By contrast, FaqId is just a technical identifier and generally adds no semantic value for response generation, so including it wastes tokens.
That is why Option A is better than the others:
* Option A keeps the meaningful text fields and removes the low-value identifier.
* Option B is too minimal because it includes only the answer text as Prompt, which strips away the product and question context the LLM may need for accurate grounding.
* Option C keeps FaqId but omits ProductName, which can be important disambiguating context.
* Option D includes everything, but that does not minimize token usage because it keeps the unnecessary FaqId.
NEW QUESTION # 35
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.
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:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.
NEW QUESTION # 36
You have an SDK-style SQL database project stored in a Git repository. The project targets an Azure SQL database.
The CI build fails with unresolved reference errors when the project references system objects.
You need to update the SQL database project to ensure that dotnet build validates successfully by including the correct system objects in the database model for Azure SQL Database.
Solution: Add the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package to the project.
Does this meet the goal?
Answer: B
Explanation:
This does meet the goal. Microsoft documents that SDK-style SQL projects can add the master.dacpac database reference as a package reference , and for Azure SQL Database the correct package is the Azure- specific master DACPAC package. The Azure SQL system DACPACs are available through NuGet, and this is the recommended way to include the right system objects in the database model for dotnet build validation.
So for an SDK-style SQL database project that targets Azure SQL Database, adding Microsoft.SqlServer.
Dacpacs.Azure.Master is the correct fix for unresolved references to system objects.
NEW QUESTION # 37
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