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It-Passports世界は急速に変化しており、Microsoft従業員に対する要件はこれまでになく高くなっています。 理想的な仕事を見つけて高収入を得たい場合は、優れた労働能力と深い知識を高めなければなりません。 DP-800のDeveloping AI-Enabled Database Solutions認定に合格すると、夢を実現できます。 製品を購入すると、最高のDeveloping AI-Enabled Database Solutions学習教材が提供され、Developing AI-Enabled Database Solutions認定の取得に役立ちます。 当社の製品は高品質であり、当社のサービスは完璧です。
質問 # 86
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 (Pll), secrets, and query result sets with any Al 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?
正解：A
解説：
The correct answer is D because the policy explicitly prohibits sharing customer PII, secrets, and query result sets with any AI service. The safe way to use GitHub Copilot Chat here is to provide only schema- level information such as table names, column names, relationships, and the required procedure behavior, without sharing actual table contents or result sets. That lets Copilot help generate and review the Transact- SQL while avoiding disclosure of customer data. This is consistent with Microsoft and GitHub guidance that content provided in prompts is what the AI can use, so avoiding real data in the prompt is the appropriate control.
The other options violate the requirement:
* A pastes real rows containing email addresses, which is direct PII disclosure.
* B shares actual query result sets, which the policy forbids.
* C provides the connection string so Copilot can validate against the database, which is inappropriate because it exposes connection details and could enable access beyond schema-only assistance.
So the correct approach is to ask Copilot to generate the stored procedure using only the schema and requirements, not real customer data.
質問 # 87
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?
正解：A
解説：
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.
質問 # 88
You have an Azure SQL database.
You deploy Data API builder (DAB) to Azure Container Apps by using the
mcr.microsoft.com/azure-databases/data-api-builder:latest image.
You have the following Container Apps secrets:
- MSSQL_CONNECTION_STRING that maps to the SQL connection string
- DAB_CONFIG_BASE64 that maps to the DAB configuration
You need to initialize the DAB configuration to read the SQL connection string.
Which command should you run?
正解：D
解説：
To initialize the Data API builder (DAB) configuration to read the SQL connection string from your Container Apps secret, use the following dab init command:
dab init --database-type mssql --connection-string "@env('MSSQL_CONNECTION_STRING')" Why this command works
--database-type mssql: Specifies that you are connecting to an Azure SQL or SQL Server database.
@env('MSSQL_CONNECTION_STRING'): This is the built-in DAB function that tells the runtime to substitute the value of the specified environment variable at load time. Since your Container Apps secret is mapped to MSSQL_CONNECTION_STRING, DAB will resolve it automatically when the container starts.
--connection-string: This flag sets the data source connection. By using the @env() syntax here, you ensure the secret remains out of the static configuration file.
Reference:
https://learn.microsoft.com/en-us/azure/data-api-builder/command-line/dab-init
質問 # 89
Your development team uses GitHub Copilot Chat in Microsoft SQL Server Management Studio (SSMS) to generate and run Transact-SQL queries against an Azure SQL database named DB1.
DB1 contains tables that store sensitive customer data.
You need to ensure that any Transact-SQL queries that run from GitHub Copilot Chat in SSMS are restricted by the same permissions as the developer's database login.
What prevents the GitHub Copilot Chat-run queries from accessing data beyond the developer's access?
正解：B
解説：
GitHub Copilot Chat in SSMS acts as an extension of the user, meaning it does not have its own separate service account or elevated privileges.
It operates within the security context of your active connection. If your database login is restricted by Role-Based Access Control (RBAC), Row-Level Security (RLS), or specific DENY permissions on sensitive tables, Copilot cannot bypass those hurdles to fetch or manipulate data you couldn't otherwise access manually.
Reference:
https://learn.microsoft.com/en-us/ssms/github-copilot/chat
質問 # 90
You have a database that contains production data. The schema is stored in a Git repository as an SDK-style SQL database project and contains the following reference data.
Name
Data type
RefID
int, identity
Code
nvarchar(10)
CreatedDate
datetime2
Description
nvarchar(255)
A deployment pipeline can be rerun automatically when a transient failure occurs.
You need to deploy the reference data as part of the same CI/CD process. Rerunning the pipeline must produce the same outcome and must NOT create duplicate rows.
What should you do?
正解：C
質問 # 91
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