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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Available Languages: | English, Arabic (Saudi Arabia), Portuguese (Brazil), French, Chinese (Simplified), Italian, Spanish, Japanese, Chinese (Traditional), German, Indonesian (Indonesia), Korean |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: SQL AI Developer Associate |
| Passing Score: | 700 |
| Exam Format: | Multiple select, Build list, Multiple choice, Drag and drop, Case studies |
| Certificate Validity Period: | 1 year |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or in-person at a testing center |
| Pre Condition: | No specific prerequisites, but candidates should have experience with SQL and a basic understanding of Azure services. Familiarity with AI or machine learning concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-800/ |
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NEW QUESTION # 57
Your development team uses Microsoft Visual Studio Code with the MSSQL extension and the GitHub Copilot Chat extension.
The team connects to an Azure SQL database by using individual database logins and uses the
@mssql chat participant to generate and run Transact-SQL queries from prompts.
What is used to ensure that GitHub Copilot Chat-generated queries run in the context of the developer?
Answer: D
Explanation:
To ensure that GitHub Copilot Chat-generated queries run in the context of a specific developer when using the @mssql chat participant, SQL Permissions must be used.
Why SQL Permissions are the Key
When the @mssql extension executes a query generated by Copilot, it uses the active connection currently established in VS Code. Because your team uses individual database logins, the execution context is governed by the following:
Authentication: The developer logs in with their specific credentials.
Authorization: The SQL Server engine checks the SQL Permissions (GRANT/DENY/REVOKE) assigned to that specific database user.
Execution: Any T-SQL command sent by the Copilot chat participant is limited by what that specific login is allowed to do (e.g., SELECT, UPDATE, or DROP).
Reference:
https://learn.microsoft.com/en-us/sql/tools/visual-studio-code-extensions/github-copilot/limitations- and-known-issues
NEW QUESTION # 58
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: C
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 # 59
Case Study 1 - Contoso
Existing Environment
Azure 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 FeedbackJsoncolumn has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the UNMASKpermission.
Problem Statements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following:
* AI 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.
You review the execution plan and discover that the plan shows a clustered index scan.
VehicleIncidentReportsoften contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Requirements
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support AI-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following security requirements:
* Restrict the support staff from viewing Personally Identifiable Information (PII) data, which is full email addresses and phone numbers.
* Enforce row-level filtering so that analysts see only incidents for the fleets to which they are assigned. The analysts can be assigned to multiple fleets.
Database Performance and 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 accuracyJSON 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 MaintenanceEventstable updates the corresponding value in the LastModifiedUtccolumn to the time of the change.
* Avoid recursive updates.
AI Search, Embeddings, and Vector Indexing
Contoso plans to implement semantic search over incident data to meet the following requirements:
* Embeddings must be stored in dedicated Azure SQL Database tables.
* Embeddings must be generated from rich natural language fields.
* Chunking must preserve semantic coherence.
* Hybrid search must combine the following:
- Vector similarity
- Keyword filtering or boosting
Development Requirements
The development team 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 FeedbackJsoncolumn of the CustomerFeedbacktable:
* 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.
Drag and Drop Question
You need to meet the database performance requirements for maintenance data.
How should you complete the Transact-SQL code? 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 # 60
You have an Azure SQL database that contains a table named Table1. Table1 contains 25,000,000 rows of data and a datetime2 column named DateKey. The data in Table1 spans the years 2020 through 2021.
You need to partition the data in Table1 by year. The solution must minimize how long it takes to rebuild or reindex the table.
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:
* Partition range # RANGE RIGHT
* Boundary values # ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The correct configuration is to use RANGE RIGHT with boundary values at the start of each year :
CREATE PARTITION FUNCTION PartitionByYear (datetime2)
AS RANGE RIGHT
FOR VALUES (
' 2020-01-01 00:00:00 ' ,
' 2021-01-01 00:00:00 '
);
Microsoft documents that with RANGE RIGHT , each boundary value belongs to the partition on its right .
For date-based partitioning, this is the natural pattern because a boundary such as 2021-01-01 becomes the lower boundary of the 2021 partition.
With these boundaries, the resulting ranges are effectively:
* Partition 1: dates before 2020-01-01
* Partition 2: 2020-01-01 through before 2021-01-01
* Partition 3: 2021-01-01 and later
This cleanly separates the 2020 and 2021 data into year-aligned partitions. Microsoft specifically recommends RANGE RIGHT for date-based boundaries because the first day of a period remains in the same partition as the rest of that period.
The other boundary choices are incorrect or less appropriate:
* Using year-end timestamps with RANGE LEFT is more cumbersome and can be sensitive to datetime2 precision.
* Monthly boundaries would partition by month, not year.
* Including 2019 and a 2021-12-31 23:59:59 boundary creates unnecessary partitions and is not the cleanest year-based design.
Therefore:
* First dropdown: RANGE RIGHT
* Second dropdown: ' 2020-01-01 00:00:00 ' , ' 2021-01-01 00:00:00 '
NEW QUESTION # 61
You need to recommend a solution to lesolve the slow dashboard query issue. What should you recommend?
Answer: D
Explanation:
The best recommendation is B because the slow query filters on FleetId and returns LastUpdatedUtc , EngineStatus , and BatteryHealth . A nonclustered index with FleetId as the key column allows the optimizer to perform an index seek instead of a clustered index scan, and including the other selected columns makes the index covering , which reduces extra lookups and I/O. Microsoft's SQL Server indexing guidance states that a nonclustered index with included columns can significantly improve performance when all query columns are available in the index, because the optimizer can satisfy the query directly from the index.
The query is:
SELECT VehicleId, LastUpdatedUtc, EngineStatus, BatteryHealth
FROM dbo.VehicleHealthSummary
WHERE FleetId = @FleetId
ORDER BY LastUpdatedUtc DESC;
Among the given choices, FleetId is the most important search argument because it appears in the WHERE predicate. Microsoft's index design guidance recommends putting columns used for searching in the key and using nonkey included columns to cover the rest of the query efficiently.
Why the other options are weaker:
* A is not appropriate because changing the clustered index to LastUpdatedUtc would not target the main filter predicate on FleetId, and a table can have only one clustered index.
* C makes LastUpdatedUtc the key, which is poor for a query whose primary filter is FleetId.
* D is not the right answer here because the query requirement does not specify only recent rows, and filtered indexes are meant for a well-defined subset; this option also uses a time-based expression that is not aligned to the stated query pattern.
Strictly speaking, the most optimal design for both filtering and ordering would usually be a composite key like (FleetId, LastUpdatedUtc), but since that is not one of the available options, B is the correct exam answer.
NEW QUESTION # 62
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