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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Exam Price: | $165 USD |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 (on a scale of 1-1000) |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Exam Format: | Case studies, Multiple choice, Drag and drop, Active screen, Multiple select |
| Available Languages: | Portuguese (Brazil), Japanese, Chinese (Simplified), French, German, Korean, English, Spanish |
| 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 # 94
Your company has an ecommerce catalog in a Microsoft SQL Server 202b database named SalesDB SalesDB contains a table named products, products contains the following columns:
* product.id (int)
* product_name (nvarchar(200))
* description (nvarchar(max))
* category (nvarchar(50))
* brand (nvarchar(W))
* price (decimal)
* sku (nvarchar(40))
The description fields ate updated dairy by a content pipeline, and price can change multiple times per day.
You want customers to be able to submit natural language queries and apply structured filters for brand and price. You plan to store embeddings in a new VECTOR(1536) column and use VECTOR_SEARCH(...
METRIC=' cosine ' ...).
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 first statement is Yes . Embeddings are used to represent the semantic meaning of content, and vector search is for conceptually similar matches over that content. Here, the semantically meaningful fields are product_name, category, and description. Using those together supports natural-language search, while brand and price can be handled as structured filters outside the embedding itself. This is an inference from Microsoft's guidance that vector search works over embeddings representing content meaning, while filters remain part of the nonvector query pipeline.
The second statement is No . price changes multiple times per day and is a structured numeric attribute, not stable semantic content. Since the requirement already says customers can apply structured filters for brand and price , price does not need to be embedded into the text. Embedding volatile numeric values would also make embeddings stale faster without improving the semantic-search objective. This is again an inference grounded in Microsoft's distinction between vector similarity over content and filtering/sorting over nonvector fields.
The third statement is Yes . In SQL Server's vector type, the default underlying base type is float32 unless float16 is specified explicitly.
NEW QUESTION # 95
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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: Build the project by running dotnet build -bl -flp:v=diag.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* Add the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package to the project.
To resolve system reference errors in an SDK-style SQL project targeting Azure SQL Database, you need to add a reference to the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package.
In your .sqlproj file, include the following item group:
<ItemGroup>
<PackageReference Include="Microsoft.SqlServer.Dacpacs.Azure.Master" Version="1.60.0" />
</ItemGroup>
Why this works:
System Objects: Standard SDK-style projects don't automatically include system views (like sys.database_principals or sys.dm_db_resource_stats). This package provides the necessary metadata for the compiler.
Azure Specifics: It includes Azure-only system objects that aren't present in the standard master database dacpac used for on-premises SQL Server.
CI/CD Friendly: Since it is a NuGet package, the dotnet build command will automatically restore it during the CI process without requiring manual file paths or local installations of Visual Studio.
Incorrect:
* Add an artifact reference to the Azure SQL Database master.dacpac file.
* Add the Microsoft.SqlServer.Dacpacs.Master NuGet package to the project.
* Build the project by running dotnet build -bl -flp:v=diag.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sql-database-projects/concepts/system-objects
NEW QUESTION # 96
You have an Azure SQL database named SalesDB that contains a table named dbo. Articles, dbo.Articles contains two million articles with embeddmgs. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARQi
Users report that semantic search results do NOT reflect the updates until the following day.
Vou need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB Which embedding maintenance method should you implement?
Answer: C
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.
NEW QUESTION # 97
You have an Azure SQL database named SalesDB that contains a table named dbo.Articles.
dbo.Articles contains two million articles with embeddings. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARCH.
Users report that semantic search results do NOT reflect the updates until the following day.
You need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB.
Which embedding maintenance method should you implement?
Answer: D
Explanation:
This setup is a solid way to bridge the gap between your SQL updates and your vector index. By using CDC (Change Data Capture), you avoid the heavy lifting of scanning the entire table for changes, which keeps your database CPU overhead low.
To make this work efficiently within the Azure Functions app, you'll typically want to:
Batch the updates: Instead of triggering a function for every single row change, process small batches of CDC records to reduce the number of calls to your embedding model (like OpenAI).
Use an Upsert logic: In your function, ensure the code handles both new inserts and updates by overwriting the existing embedding for a specific Article ID in the database.
Implement a "Cool-down" or Timer: If the "frequent updates" happen in bursts, you can use an Azure Service Bus or Storage Queue between the CDC process and the Function to smooth out the processing load.
Reference:
https://fivetran.com/docs/connectors/databases/troubleshooting/log-based-cdc-impact
NEW QUESTION # 98
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 # 99
......
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