DP-800 Valid Exam Book - Valid DP-800 Test Pdf

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Microsoft DP-800 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI-Enabled Database Solutions
Exam Number:DP-800
Real Exam Qty:40-60
Certificate Validity Period:1 year (renewable annually via free online assessment)
Passing Score:700 (on a scale of 1-1000)
Exam Format:Drag and drop, Multiple choice, Multiple select, Case studies, Active screen
Exam Duration:100 minutes
Exam Price:$165 USD
Available Languages:German, Korean, Japanese, Portuguese (Brazil), Chinese (Simplified), Spanish, French, English
Recommended Training:Microsoft Learn DP-800 Learning Path
Exam Registration:Pearson VUE Registration
Sample Questions:Microsoft DP-800 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended experience: T-SQL development, SQL Server/Azure SQL, CI/CD practices, basic AI concepts
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800

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Microsoft DP-800 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 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.
Topic 3
  • 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.

Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q27-Q32):

NEW QUESTION # 27
Drag and Drop Question
You have an Azure AI Search service and an index named hotels that includes a vector field named DescriptionVector.
You query hotels by using the Search Documents REST API.
You need to implement a hybrid search query that uses DescriptionVector and includes captions.
How should you complete the REST request body? 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.

Answer:

Explanation:


NEW QUESTION # 28
Your team is 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 uses the team's coding standards when generating Transact-SQL code in Visual Studio Code.
What should you use?

Answer: B

Explanation:
o ensure GitHub Copilot Chat adheres to your team's specific Transact-SQL (T-SQL) standards while deploying an Azure SQL database from VS Code, you should focus on Custom Instructions and Workspace Context.
Required Setup
Create a .github/copilot-instructions.md File
This is the most effective way to enforce standards.
Create this file in the root of your repository.
Copilot automatically reads this file to understand project-specific rules.
Include sections for:
Naming conventions (e.g., PascalCase for tables, proc_ prefix for stored procedures).
Formatting rules (e.g., keywords in UPPERCASE, use of 4 spaces).
Security practices (e.g., always use schema prefixes, avoid SELECT *).
Reference:
https://nikolay-dev.medium.com/master-web-development-with-github-spark-ai-b0b874525418


NEW QUESTION # 29
You have an Azure SQL database that contains a table named dbo.Orders.
You have an application that calls a stored procedure named dbo.usp_tresteOrder to insert rows into dbo.
Orders.
When an insert fails, the application receives inconsistent error details.
You need to implement error handling to ensure that any failures inside the procedure abort the transaction and return a consistent error to the caller.
How should you complete the stored procedure? To answer, drag the appropriate values to the correct targets, tach 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.

Answer:

Explanation:

Explanation:
* After the INSERT # SET @OrderId = SCOPE_IDENTITY()
* Inside CATCH # IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION
The correct drag-and-drop choices are:
* SET @OrderId = SCOPE_IDENTITY()
* IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION
After the INSERT, the procedure should assign the newly generated identity value to the output parameter by using SCOPE_IDENTITY() . Microsoft documents that SCOPE_IDENTITY() returns the last identity value inserted in the same scope, which makes it the correct choice for returning the new OrderId from the procedure.
Inside the CATCH block, the procedure should use IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION before THROW. This ensures any open transaction is rolled back only when one actually exists, which prevents transaction-state issues and guarantees the failure aborts the transaction cleanly.
Keeping THROW after the rollback is also the correct modern pattern because THROW re-raises the error to the caller with the original error information intact, giving consistent error behavior. This matches SQL Server best practice for TRY...CATCH transaction handling.


NEW QUESTION # 30
You have an Azure SQL database named SalesDB that has Query Store enabled SalesDB supports an Al- driven product search API.
User? report that the latency of the API increased immediately after the API was deployed and remains high.
You need to identity whether the latency increase was caused by an execution plan regression and, if so, decide which corrective action will restore a previous plan The solution must prevent any changes to the API code.
What should you use? To answer, drag the appropriate tools to the correct step. Each tool may be used once, more than once, or not at all. You may need to drag the split bat between panes or scroll to view content.
NOTE: Each correct selection is worth one Point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 31
You have an Azure SQL database that contains the following SQL graph tables:
* A NODE table named dbo.Person
* An EDGE table named dbo.Knows
Each row in dbo.Person contains the following columns:
* Personid (int)
* DisplayName (nvarchar(100))
You need to use a HATCH operator and exactly two directed Knows relationships to return the Personid and DisplayName of people that are reachable from the person identified by an input parameter named
@startPersonid.
Which Transact-SQL query should you use?

Answer: B

Explanation:
The correct query is Option D because it starts from the input person and uses exactly two directed Knows edges in a single MATCH pattern:
MATCH(p1-(k1)- > p2-(k2)- > p3)
Microsoft documents that SQL Graph uses the MATCH predicate in the WHERE clause to express graph traversal patterns over node and edge tables, and directed relationships are written with arrow syntax such as node1-(edge)- > node2.
Why D is correct:
* It anchors the starting node with p1.PersonId = @StartPersonId.
* It traverses two directed hops : p1 - > p2 - > p3.
* It returns p3.PersonId, p3.DisplayName, which are the people reachable in exactly two Knows relationships.
Why the others are wrong:
* A filters on DisplayName = DisplayName, which is unrelated to the required input parameter and does not correctly anchor the start node.
* B reverses the traversal direction in the pattern.
* C uses two separate MATCH predicates instead of the required single two-hop directed pattern. The proper graph pattern syntax supports chaining the hops directly in one MATCH expression.
Topic 1, Contoso Case Study
Existing Environment
Contoso has an Azure subscription in North Europe that contains the corporate infrastructure. The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.

The FeedbackJson column has a full-text index and stores JSON documents in the following format.

The support staff at Contoso never has the unmask permission.
Requirements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following;
* Al workloads
* Vector search
* Modernized API access
* Retrieval Augmented Generation (RAG) pipelines
Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.
SELECT VehicleTd, Lastupdatedutc, EngineStatus, BatteryHealth FROM dbo.VehicleHealthSumary where fleetld - gFleetld ORDER BV LastUpdatedUtc DESC; You review the execution plan and discover that the plan shows a clustered index scan.
vehicleincidentReports often contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support Al-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following telemetry requirements:
* Telemetry data must be stored in a partitioned table.
* Telemetry data must provide predictable performance for ingestion and retention operations.
* latitude, longitude, and accuracy JSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
* Ensure that any changes to a row in the MaintenanceEvents table updates the corresponding value in the LastModif reduce column to the time of the change.
* Avoid recursive updates.
AI Search, Embedding's, and Vector indexing
The development learn at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases. Contoso identifies the following requirements for querying data in the FeedbackJson column of the customer-Feedback table:
* Extract the customer feedback text from the JSON document.
* Filter rows where the JSON text contains a keyword.
* Calculate a fuzzy similarity score between the feedback text and a known issue description.
* Order the results by similarity score, with the highest score first.


NEW QUESTION # 32
......

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