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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Developing AI-Enabled Database Solutions (DP-800) |
| Exam Number: | DP-800 |
| Certificate Validity Period: | 1 year (Microsoft role-based certifications require annual renewal) |
| Exam Format: | Case studies, Multiple choice |
| Available Languages: | English |
| Passing Score: | 700/1000 (typical Microsoft passing score; subject to confirmation) |
| Recommended Training: | Microsoft Learn - Azure Data & AI Learning Paths |
| Exam Registration: | Microsoft Certification Dashboard |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No formal prerequisites required, but familiarity with Azure data services and database concepts is recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/ |
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NEW QUESTION # 93
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the .dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets.
What should you include in the workflow?
Answer: B
Explanation:
To deploy your .dacpac to Azure SQL using GitHub Actions, you should use the official azure/sql- action@v2. This action is designed specifically to take the output of your dotnet build and publish it.
Assuming your build step is already working, here is how you structure the deployment job.
GitHub Actions Workflow Snippet
Add this job to your .yml file. It depends on your build job (usually named build) and runs on a runner with the Azure CLI installed (like ubuntu-latest).
- name: Deploy SQL Schema
uses: azure/sql-action@v2
with:
connection-string: ${{ secrets.AZURE_SQL_CONNECTION_STRING }}
path: './bin/Release/netstandard2.1/YourDatabase.dacpac' # Path to your .dacpac action: 'publish' Incorrect:
[Not A]
Use connection-string: ${{ secrets.AZURE_SQL_CONNECTION_STRING }}
[Not B]
Action set to publish, not to extract.
[Not D]
Use azure/sql-action@v2.
Reference:
https://stackoverflow.com/questions/75490960/github-actions-dotnet-publish-specify-a-project- with-a-dot-in-the-name
NEW QUESTION # 94
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.
NEW QUESTION # 95
You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1. DB1 contains tables named Sales.Orders and Sales.Customers.
You use GitHub Copilot Chat in the context of DB1.
A company policy prohibits sharing customer Personally Identifiable Information (PII), secrets, and query result sets with any AI service.
You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to Sales.Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.
What should you do?
Answer: D
Explanation:
To use GitHub Copilot Chat effectively in this environment without exposing sensitive data, you should focus your prompts entirely on the schema and logic rather than the data itself.
Since Copilot Chat can "see" your open files, the best approach is to provide the table structures as DDL (Data Definition Language) statements or a simplified description.
Steps to generate the stored procedure:
Define the Schema: Open a new SQL file in your Codespace. Paste the CREATE TABLE scripts (without any data) for your two tables.
Prompt Copilot: Use the Chat view to request the procedure.
Example Prompt: "Based on the table definitions in my open file, write a T-SQL stored procedure that joins TableA and TableB on [Join Column]. Include logic to filter by [Parameter] and ensure no PII columns are included in the SELECT statement." Review for PII/Secrets: Before executing, manually verify that the generated code doesn't include hardcoded secrets or call PII columns you intended to omit.
Security Check: Because you are in a Codespace, ensure your .env files or connection strings are in your .gitignore so they aren't indexed by Copilot.
Reference:
https://github.com/orgs/community/discussions/141924
NEW QUESTION # 96
You have a SQL database in Microsoft Fabric that contains the following functions:
* A multi-statement table-valued function (TVF) named sales.mstvf_orderStatus() that returns order status information
* A scalar user-defined function (UOF) named dbo.ufn_GetlaxMultiplier(