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By overcoming your mistakes before the actual Microsoft DP-800 exam, you can avoid making those same errors during the Developing AI-Enabled Database Solutions (DP-800) real test. With customizable DP-800 practice tests, you can adjust the duration and quantity of DP-800 Practice Questions. This self-assessment DP-800 exam display your marks, helping you improve your performance while tracking your progress.
| 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) |
| Exam Duration: | 100 minutes |
| Exam Format: | Multiple choice, Multiple select, Case studies, Active screen, Drag and drop |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (on a scale of 1-1000) |
| Exam Price: | $165 USD |
| Available Languages: | Korean, Spanish, English, French, Chinese (Simplified), Japanese, German, Portuguese (Brazil) |
| 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 # 46
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders. dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.Orders.
Solution: Identify the partition scheme for the oldest month, and then run the following Transact-SQL statement:
SQL
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME {partition_scheme_name};
Does this meet the goal?
Answer: B
NEW QUESTION # 47
You have an Azure SQL database that supports a customer-facing API. The API calls a stored procedure named dbo.GetCustomerOrders thousands of times per hour.
After a deployment that updated indexes and statistics, users report that the API endpoint backed by dbo.
Getcustomerorders is slower. In Query Store, the same query now has two persisted execution plans. During the last hour, the newer plan had a significantly higher average duration and CPU time than the older plan.
You need to restore the previous performance quickly, without changing the API code.
Which Transact-SQL command should you run?
Answer: C
Explanation:
The scenario says Query Store already shows two persisted execution plans for the same query, and the older plan performed much better than the newer one during the last hour. Microsoft documents that sp_query_store_force_plan is used to force a particular plan for a particular query in Query Store. That makes it the fastest way to restore the previously good plan without changing application code, which is exactly what the question requires.
Why the other options are not the best fit:
sp_query_store_set_hints is for adding or updating Query Store hints to influence compilation behavior, but when you already know the exact older good plan, Microsoft points to plan forcing as the direct remedy.
DBCC FREEPROCCACHE clears cached plans broadly and is disruptive; it does not guarantee a return to the known good plan.
ALTER DATABASE is too general and does not directly restore the prior execution plan.
So the right Transact-SQL command is:
EXEC sp_query_store_force_plan
using the relevant @query_id and @plan_id from Query Store for the older, better-performing plan.
Microsoft also notes that when a plan is forced, SQL Server tries to use that plan whenever it encounters the query again.
NEW QUESTION # 48
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 leferences 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 an artifact reference to the Azure SQL Database master.dacpac file.
Does this meet the goal?
Answer: B
Explanation:
For an SDK-style SQL database project targeting Azure SQL Database , Microsoft recommends using the Azure SQL system DACPAC as a NuGet package reference rather than adding a direct artifact reference to master.dacpac for new SDK-style development. Microsoft's SQL Database Projects documentation says direct .dacpac artifact references are not recommended for new development in SDK-style projects ; instead, use NuGet package references .
Because the goal is specifically to make dotnet build validate successfully with the correct Azure SQL system objects , adding an artifact reference to master.dacpac is not the recommended SDK-style solution. It can work in some project styles, but it does not meet the stated goal as the proper approach for SDK-style Azure SQL projects.
NEW QUESTION # 49
You have an Azure SQL database that contains a table named dbo.Products. dbo.Products contains three columns named Embedding, Category, and Price. The Embedding column is defined as VECTOR(1536).
You use AI_GENERATE_EMBEDDINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3- small. Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?
Answer: B
Explanation:
To ensure your applications can transition models without runtime errors while using VECTOR_SEARCH, you must first define a Vector Index that explicitly identifies the dimensions and distance metric.
Since you are moving from text-embedding-ada-002 to text-embedding-3-small, both models default to 1536 dimensions, which matches your existing column definition. To create the index as the first step, use the following SQL:
CREATE VECTOR INDEX idx_embedding ON YourTableName (Embedding)
WITH ( DISTANCE_METRIC = 'COSINE' );
Use code with caution.
Why this works:
Schema Consistency: Because both models use 1536 dimensions, you don't need to alter the VECTOR(1536) column type immediately.
Search Stability: Creating the index allows the engine to optimize the VECTOR_SEARCH function. As long as the incoming query vector (generated by the app) matches the dimensions of the stored vectors, the search will execute without a runtime dimension mismatch error.
Reference:
https://docs.couchbase.com/cloud/n1ql/n1ql-language-reference/vectorfun.html
NEW QUESTION # 50
You have a database named DB1. The schema is stored in a GitHub repository as an SDK style SQL database project.
You use a feature branch workflow to deploy changes to DB1
You need to update the local feature branch with the latest changes to main, and then create a pull request to merge the feature branch into main for review.
How should you complete the GitHub CLI script? 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 sequence is:
* git fetch origin
* git merge origin/main
* gh pr create
This is the right workflow because the script starts on the local feature branch:
git checkout feature/db1-add-staticdata
To update that local feature branch with the latest changes from main, you first fetch the latest remote refs with git fetch origin , then merge the updated remote main branch into the current feature branch with git merge origin/main . After the feature branch is up to date, the correct GitHub CLI command to open the pull request is gh pr create . GitHub's CLI documentation shows that gh pr create is the command used to create a pull request, and supports flags such as --title, --body, --head, --base, --repo, and --web, which match the script shown in the question.
The other commands are not the best fit here:
* git checkout main would move you off the feature branch, which is not what you want before merging main into the feature branch.
* git pull origin main could update from remote main, but the script pattern here clearly separates fetching and then merging.
* gh pr merge merges an existing pull request, not create one.
* gh pr ready marks a draft PR as ready for review, but does not create the PR.
NEW QUESTION # 51
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