DP-800 - Developing AI-Enabled Database Solutions Perfect Test Registration

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Microsoft DP-800 Exam Syllabus Topics:

SectionWeightObjectives
Implement AI capabilities in database solutions25-30%- Integrate AI models and services
  • 1. Implement vector data and embeddings
  • 2. Integrate with Azure AI services and Microsoft Foundry
  • 3. Create and manage external models
- Build intelligent search and retrieval
  • 1. Design and implement embedding pipelines
  • 2. Implement semantic search and RAG patterns
  • 3. Optimize search performance and relevance
- Use AI-assisted development tools
  • 1. Leverage AI tools for T-SQL development
  • 2. Validate and test AI-enhanced solutions
Design and develop database solutions35-40%- Design database solutions
  • 1. Design solutions for structured and semi-structured data
  • 2. Design for maintainability and DevOps integration
  • 3. Design for scalability and performance
- Develop database solutions
  • 1. Write and optimize T-SQL code
  • 2. Implement data integration and transformation
  • 3. Implement database objects and structures
Secure, optimize, and deploy database solutions35-40%- Optimize performance and reliability
  • 1. Ensure high availability and disaster recovery
  • 2. Optimize queries and indexes
  • 3. Monitor and troubleshoot performance
- Secure database solutions
  • 1. Implement authentication and authorization
  • 2. Protect data at rest and in transit
  • 3. Implement compliance and auditing
- Deploy and maintain solutions
  • 1. Manage updates and versioning
  • 2. Implement CI/CD for databases
  • 3. Deploy across SQL Server, Azure SQL, and Microsoft Fabric

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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q59-Q64):

NEW QUESTION # 59
Hotspot Question
You have an Azure SQL database named ProductsDB.
You deploy Data API builder (DAB) to Azure Container Apps.
You discover that the container app cannot connect to ProductsDB.
Your development team reports that the container app is unreachable from the internet for integration tests.
You need to update Azure SQL Database and Container Apps to meet the following requirements:
- Ensure that the Azure SQL logical server allows connections from
Azure services.
- Ensure that the Container Apps environment accepts inbound requests
from the public internet.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 60
Case Study 2 - Fabrikam
Existing Environment
Azure Environment
Fabrikam has a single Azure subscription in the East US 2 Azure region. The subscription contains an Azure SQL database named DB1. DB1 contains the following tables:
* Patients
* Employees
* Procedures
* Transactions
* UsefulPrompts
* ProcedureDocuments
You store a column master key as a secret in Azure Key Vault.
You have an on-premises application named TransactionProcessing that uses a hard-coded username and password in a connection string to access DB1.
Problem Statements
Users report that after executing a long-running stored procedure named sp_UpdateProcedureForPatient, updates to the underlying data are sometimes inconsistent.
Requirements
Planned Changes
Fabrikam plans to manage all changes to Azure SQL Database objects by using source control in GitHub. Every pull request submitted to production will be validated before it can be merged.
Deployments must use the Release configuration.
Security Requirements
Fabrikam identifies the following security requirements:
* The TransactionProcessing application must use a passwordless connection to DB1.
* The Employees table contains two columns named TaxID and Salary that must be encrypted at rest.
* Auditors must have a tamper-evident history of transactions with cryptographic proof of changes to the employee data.
Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
AI Search, Embeddings, and Vector Indexing
Fabrikam identifies the following AI-related requirements:
* Queries to the ProcedureDocuments table must use Reciprocal Rank Fusion (RRF).
* Users must be able to query the data in DB1 by using prompts in Copilot in Microsoft Fabric.
* The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
Development Requirements
Fabrikam identifies the following development requirements:
* Provide the functionality to retrieve all the transactions of a given patient between two dates, showing a running total.
* Expose a Data API builder (DAB) configuration file to enable Azure services to perform the following operations over a REST API:
- Read data from the procedures table without authentication.
- Read and insert data into the Transactions table once authenticated.
- Execute the sp_UpdateProcedurePatient stored procedure.
* Provide the functionality to retrieve a list of the names of patients who underwent medical procedures during the last 30 days.
* Information for each medical procedure will be stored in a table. The table will be used with a large language model (LLM) for user querying and will have the following structure.

DAB
You create a DAB configuration file that meets the development requirements for DB1 and includes the following entities.

You need to use the UsefulPrompts table as defined in the AI requirements. Which stored procedure should you use?

Answer: B

Explanation:
Scenario:
The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
The system stored procedure sp_invoke_external_rest_endpoint is used to connect an Azure SQL Database to an Azure OpenAI endpoint.This procedure allows you to call HTTPS REST endpoints directly from your database, enabling the integration of generative AI or embedding models into your SQL workflows without an intermediate application layer.
Reference:
https://blog.fabric.microsoft.com/en-gb/blog/ai-ready-apps-from-rag-to-chat-interacting-with-sql- database-in-microsoft-fabric-using-graphql-and-mcp


NEW QUESTION # 61
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
Create a vector index on the appropriate column.
Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Verified Answer : =
* Vector index column # embedding
* Vector search function # VECTOR_SEARCH
* Final clause # ORDER BY s.distance
The first selection is embedding because CREATE VECTOR INDEX must target the column that stores vector data. Microsoft documents the syntax as CREATE VECTOR INDEX ... ON object (vector_column) , so the index belongs on dbo.Products.embedding , not on distance or product_name .
The second selection is VECTOR_SEARCH . This function is designed for vector similarity retrieval and works with vector indexes in SQL database in Microsoft Fabric . In the shown code, the supplied
@query_vector VECTOR(1536) is compared against the embedding column using cosine distance, and TOP_N = 10 requests the ten nearest matches. Microsoft specifically documents VECTOR_SEARCH for approximate nearest-neighbor searches and its integration with vector indexes.
The final clause is ORDER BY s.distance because VECTOR_SEARCH returns a distance value representing how far each stored vector is from the supplied query vector. With cosine distance, smaller distance means greater similarity , so ascending order returns the most relevant products first.
Therefore, the completed selections are:
CREATE VECTOR INDEX idx_products_embedding
ON dbo.Products (embedding)
WITH (METRIC = ' cosine ' , TYPE = ' DiskANN ' );
FROM VECTOR_SEARCH (
TABLE = dbo.Products AS t,
COLUMN = embedding,
SIMILAR_TO = @query_vector,
METRIC = ' cosine ' ,
TOP_N = 10
) AS s
ORDER BY s.distance;
So the verified hotspot answers are embedding # VECTOR_SEARCH # ORDER BY s.distance .


NEW QUESTION # 62
You have an SDK-style SQL database project named MyDatabaseProject.sqlproj stored in a private GitHub repository. The repository contains the following GitHub Actions workflow:
YAML
name: Build and Deploy SQL Project
on:
push:
branches:
- main
workflow_dispatch:
jobs:
build-and-deploy:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@v4
- name: Build SQL project
run: dotnet build MyDatabaseProject.sqlproj
- name: Publish DACPAC
uses: azure/sql-action@v2

Answer:

Explanation:

Explanation:


NEW QUESTION # 63
Drag and Drop Question
You have an Azure SQL database named SalesDB that supports an ecommerce application.
SalesDB contains a table named dbo.Orders that has a clustered index on a column named OrderId.
dbo.Orders receives continuous OLTP inserts and updates during business hours.
Your analytics team runs hourly aggregate queries that scan dbo.Orders to calculate revenue trends for recent dates.
You need to improve the performance of the hourly analytics queries without significantly affecting OLTP throughput. The solution must meet the following requirements:
- Support near-real-time (NRT) analytics on the dbo.Orders table.
- Reduce read time when retrieving analytical data from dbo.Orders.
- Support indexing only rows that match a predicate, such as Active =
1.
Which type of index should you use for each requirement? To answer, drag the appropriate index types to the correct requirements. 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.

Answer:

Explanation:


NEW QUESTION # 64
......

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