DP-800試験の参考資料のユーザーは、専門家、学生、高度な文化の学生など、幅広い分野をカバーしています。これは、DP-800学習教材の言語形式が理解しやすいためです。どんな情報を勉強しても、初心者であることやデータを読んでいないことを心配する必要はありません。そして、DP-800テストの質問は多くの専門家によって準備されています。 DP-800学習ガイドの内容は、すべてのレベルの候補者にとって非常に簡単に理解できます。
| Section | Objectives |
|---|---|
| Topic 1: Design and implement data solutions | - Design database solutions using Azure data services - Implement data storage and data processing solutions |
| Topic 2: Monitor, troubleshoot, and maintain solutions | - Monitoring database health and performance - Troubleshooting data pipeline issues |
| Topic 3: Integrate AI capabilities with database systems | - Implement AI-assisted data processing - Use Azure AI services with database workloads |
| Topic 4: Develop and manage database solutions | - Optimize performance and scalability - Ensure security and compliance of data solutions |
PassTestのMicrosoftのDP-800試験トレーニング資料は高度に認証されたIT領域の専門家の経験と創造を含めているものです。私たちのIT専門家は受験生のために、最新的なMicrosoftのDP-800問題集を提供します。うちの学習教材の高い正確性は言うまでもありません。受験生が最も早い時間で、一回だけでMicrosoftのDP-800認定試験に合格できるために、PassTestはずっとがんばります。
質問 # 61
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.
正解：
解説：
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.
質問 # 62
Drag and Drop Question
You have an Azure SQL database that stores sales data and contains tables named Sales and Products. Sales contains three columns named SalesDate, ProductKey, and TotalSale, Sales is 10 TB and is loaded nightly by using a batch process. Most reporting queries scan large portions of Sales, filter on SalesDate or ProductKey, and use SUM() to aggregate TotalSale.
Products is relatively small and is used primarily for point lookups and joins to Sales.
You need to recommend which indexes to create to optimize the reporting queries. The solution must minimize storage requirements.
Which type of index should you recommend for each table? To answer, drag the appropriate index types to the correct tables. Each index type 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.
正解：
解説：
質問 # 63
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: Run the following Transact-SQL statement.
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(nonth, -36, SYSUTCDATETIME());
Does this meet the goal?
正解：B
解説：
This does not meet the goal. A row-by-row DELETE against the oldest month is not the lowest-impact way to purge data from a monthly partitioned table. Microsoft's partitioning guidance specifically says partitioning lets you perform maintenance and retention operations more efficiently by targeting just the relevant partition, including the ability to truncate data in a single partition .
The proposed statement:
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(month, -36, SYSUTCDATETIME());
would log row deletions and can hold locks longer, creating more overhead for other queries than a partition- level maintenance operation. Since the table is already partitioned by month , the expected low-impact approach is to operate on the oldest partition directly, not issue a broad delete predicate over rows. Microsoft explicitly highlights partition-targeted truncation as a faster, more efficient retention operation than working against the whole table or rowset.
質問 # 64
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?
正解：B
解説：
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
質問 # 65
You are developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat can call the hosted GitHub MCP Server tools by using OAuth. The MCP server configuration must be scoped to the repository.
What should you do in Visual Studio Code?
正解：D
解説：
To connect GitHub Copilot Chat to an Azure SQL Database using a hosted GitHub MCP (Model Context Protocol) Server, follow these steps. This setup ensures that Copilot can interact directly with your repository and database schema using OAuth.
Step 1: Configure the MCP Server in VS Code
You must set the scope to the Workspace level to ensure the server is restricted to your specific repository.
Open your locally cloned GitHub repository in VS Code.
Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P).
Type and select: MCP: Add Server.
Select HTTP (HTTP or Server-Sent Events).
Enter the URL: https://api.githubcopilot.com/mcp/
Crucial: When prompted for the configuration scope, select Workspace Settings.
This saves the config to .vscode/settings.json, binding the server to this repo.
Step 2: Authenticate via OAuth
Once the server is added, Copilot needs permission to act on your behalf.
Open the GitHub Copilot Chat panel.
You will see a notification or a prompt within the chat asking to Authorize the MCP server.
Follow the browser prompts to sign in and grant OAuth permissions.
This allows the hosted MCP server to "see" your repository's code and metadata.
Reference:
https://skywork.ai/skypage/en/The-Ultimate-Guide-to-GitHub's-MCP-Server:-Bridging-AI-and- Your-Codebase/1970685991323758592
質問 # 66
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証明書は私たちの日常生活で重要です。現在、DP-800試験に合格するすべての受験者に、選択可能な3つの異なるバージョンを提供しています。 DP-800試験問題のAPPバージョンは、オフライン状態で機能します。クイズ準備を使用する場合、最新のDP-800試験トレントをいつでもどこでも使用できます。オフライン状態のDP-800実践ガイドを使用して、どのようにして学習を楽しむことができますか？オフライン状態で動作するバージョンをダウンロードするだけで、初めてDP-800クイズトレントのバージョンをオンラインで使用する必要があります。
DP-800トレーニング費用: https://www.passtest.jp/Microsoft/DP-800-shiken.html