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Microsoft DP-800 Prüfungsplan:

Thema	Einzelheiten
Thema 1	Secure, optimize, and deploy database solutions: This domain focuses on implementing data security measures like encryption, masking, and row-level security, optimizing query performance, managing CI CD pipelines using SQL Database Projects, and integrating SQL solutions with Azure services including Data API builder and monitoring tools.
Thema 2	Design and develop database solutions: This domain covers designing and building database objects such as tables, views, functions, stored procedures, and triggers, along with writing advanced T-SQL code and leveraging AI-assisted tools like GitHub Copilot and MCP for SQL development.
Thema 3	Implement AI capabilities in database solutions: This domain covers designing and managing external AI models and embeddings, implementing full-text, semantic vector, and hybrid search strategies, and building retrieval-augmented generation (RAG) solutions that connect database outputs with language models.

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Microsoft Developing AI-Enabled Database Solutions DP-800 Prüfungsfragen mit Lösungen (Q73-Q78):

73. Frage
You have a Microsoft SQL Server 2025 instance that has a managed identity enabled.
You have a database that contains a table named dbo.ManualChunks. dbo.ManualChunks contains product manuals.
A retrieval query already returns the top five matching chunks as nvarchar(max) text.
You need to call an Azure OpenAI REST endpoint for chat completions. The solution must provide the highest level of security.
You write the following Transact-SG1 code.

What should you insert at line 02?

Antwort: E

Begründung:
The correct answer is Option B because the requirement is to call an Azure OpenAI REST endpoint from SQL Server 2025 while providing the highest level of security , and the instance already has a managed identity enabled . For Microsoft's SQL AI features, the preferred secure pattern is to use a database scoped credential with IDENTITY = ' Managed Identity ' instead of storing an API key. Microsoft documents that SQL Server 2025 supports managed identity for external AI endpoints, and for Azure OpenAI the credential secret uses the Cognitive Services resource identifier: { " resourceid " : " https://cognitiveservices.azure.
com " } .
So line 02 should be:
WITH IDENTITY = ' Managed Identity ' ,
SECRET = ' { " resourceid " : " https://cognitiveservices.azure.com " } ' ; Why the other options are incorrect:
* A and D use HTTP header or query-string credentials with an API key , which is less secure than managed identity because a secret key must be stored and rotated manually. Microsoft recommends managed identity where supported to avoid embedded secrets.
* C mixes Managed Identity with an api-key secret, which is not the correct pattern for Azure OpenAI managed-identity authentication.
* E uses an invalid identity value for this scenario. The accepted credential identities for external REST endpoint calls include HTTPEndpointHeaders , HTTPEndpointQueryString , Managed Identity , and Shared Access Signature .
Because the endpoint is Azure OpenAI and the question explicitly asks for the highest security , managed identity with the Cognitive Services resource ID is the Microsoft-aligned answer.

74. Frage
You have an Azure SQL database that contains a table named Customer. Customer contains the following columns:
- NationalIDNumber is unique per customer.
- InvestigationNotes contains free-form text.
You have a stored procedure that performs point lookups by NationalIDNumber and returns InvestigationNotes in the query results without filtering on InvestigationNotes.
You need to encrypt both columns by using Always Encrypted and the highest security possible.
Which type of Always Encrypted encryption should you use for each column?

A. deterministic for NationalIDNumber and deterministic for InvestigationNotes
B. deterministic for NationalIDNumber and randomized for InvestigationNotes
C. randomized for NationalIDNumber and randomized for InvestigationNotes
D. randomized for NationalIDNumber and deterministic for InvestigationNotes

Antwort: B

Begründung:
To achieve the highest security for an Azure SQL Database using Always Encrypted, you must use Randomized Encryption for the InvestigationNotes and Deterministic Encryption for the NationalIDNumber.
While "Randomized" is more secure, it does not support point lookups (equality searches).
Therefore, a hybrid approach is required to maintain functionality while maximizing protection.
Recommended Encryption Settings
* NationalIDNumber
Deterministic is required to allow the Stored Procedure to perform point lookups (WHERE NationalIDNumber = @id).
* InvestigationNotes
Use randomized as it provides the highest security. It prevents pattern matching and "leakage" for sensitive text.
Reference:
https://medium.com/@ih_hira/implementing-always-encrypted-with-azure-key-vault-and-entity- framework-core-in-azure-sql-part-1-3a0e5fd7ed9b

75. Frage
What is the role of Azure Synapse in AI + SQL solutions?

A. Email services
B. Data warehousing and analytics
C. Authentication
D. DNS management

Antwort: B

Begründung:
Azure Synapse Analytics combines big data and data warehousing for AI-driven insights.

76. Frage
You need to design a generative AI solution that uses a Microsoft SQL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
- Are grounded in the latest transactional and reference data stored in DB1
- Do NOT require retraining or fine-tuning the language model when the
data changes
- Can include citations or references to the source data used in the
response
Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern? More than one answer choice may achieve the goal. Select the BEST answer.

A. summarizing free-form user input text
B. generating marketing slogans based on user sentiment analysis
C. answering user questions based on company-specific knowledge
D. training a custom language model on historical database data

Antwort: C

Begründung:
Implementing a Retrieval-Augmented Generation (RAG) pattern with Microsoft SQL Server 2025 allows you to ground generative AI responses in real-time company data without the need for model fine-tuning. This version of SQL Server functions as a native vector database, enabling seamless integration between relational data and AI-powered insights.
Incorrect:
[Not B]
Training a custom language model is not a use case for the Retrieval Augmented Generation (RAG) pattern.
In fact, the primary purpose of RAG is to provide a language model with up-to-date information without the need for training, retraining, or fine-tuning.
Reference:
https://devblogs.microsoft.com/azure-sql/sql-server-2025-embraces-vectors-setting-the- foundation-for-empowering-your-data-with-ai

77. Frage
Drag and Drop Question
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 nvarchar (max) column named log that stores JSON documents.
You have a daily report that filters by the $.severity JSON property and returns LogId, 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.

Antwort:

Begründung:

78. Frage
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