P.S. Free & New DP-800 dumps are available on Google Drive shared by ExamCost: https://drive.google.com/open?id=1jeDYy433z_UOP84HTCv0FzwYwECfCLa8
Our considerate service is not only reflected in the purchase process, but also reflected in the considerate after-sales assistance on our DP-800 exam questions. We will provide considerate after-sales service to every user who purchased our DP-800 practice materials. If you have any questions after you buy our DP-800 study guide, you can always get thoughtful support and help by email or online inquiry. If you neeed any support, and we are aways here to help you.
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
>> Reliable Microsoft DP-800 Dumps Pdf <<
Life is always full of ups and downs. You can never stay wealthy all the time. So from now on, you are advised to invest on yourself. The most valuable investment is learning. Perhaps our DP-800 exam materials can become your top choice. Just look at the joyful feedbacks from our worthy customers who had passed their exams and get the according certifications, they have been leading a better life now with the help of our DP-800 learning guide. Come to buy our DP-800 study questions and become a successful man!
NEW QUESTION # 11
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements.
What should you include in the recommendation?
Answer: A
Explanation:
The best recommendation is to use an MCP server . In the official DP-800 study guide , Microsoft explicitly lists skills such as configuring Model Context Protocol (MCP) tool options in a GitHub Copilot session and connecting to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse . That makes MCP the exam-aligned mechanism for enabling AI-assisted tools to work with live database context rather than static snapshots.
This also matches the stated development requirement: the team will use Visual Studio Code and GitHub Copilot and needs to retrieve live metadata from the databases . Microsoft's documentation for GitHub Copilot with the MSSQL extension explains that Copilot works with an active database connection , provides schema-aware suggestions , supports chatting with a connected database, and adapts responses based on the current database context . Microsoft also documents MCP as the standard way for AI tools to connect to external systems and data sources through discoverable tools and endpoints.
The other options do not satisfy the "live metadata" requirement as well:
* A .dacpac is a point-in-time schema artifact, not live metadata.
* A Copilot instruction file provides guidance, not live database discovery.
* Including the database project in the repository helps source control and deployment, but it still does not provide live database metadata by itself.
NEW QUESTION # 12
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.
NEW QUESTION # 13
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.
You need to recommend a solution that will resolve the ingestion pipeline failure issues.
Which two actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
Scenario: Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
Adding a CHECK constraint with ISJSON and creating a unique index on a computed hash of the payload are effective measures in Azure SQL Database to handle malformed JSON and duplicate payloads.
[E]
JSON Validation with a CHECK Constraint
You can use a CHECK constraint in conjunction with the built-in ISJSON function to ensure that only valid JSON text is stored in a VARCHAR or NVARCHAR column. The constraint automatically rejects any insert or update operation that contains malformed JSON data, preventing pipeline failures due to invalid formatting at the database level.
[D]
Deduplication with a Unique Index on a Hashed Payload
To prevent duplicate payloads, you can create a unique index on a computed column that stores a hash of the entire JSON payload or relevant business keys within the JSON.
Reference:
https://www.shaped.ai/blog/10-best-practices-in-data-ingestion
NEW QUESTION # 14
You have a Microsoft SQL Servei 2025 database that contains a table named dbo.Customer-Messages, dbo.
Customer-Messages contains two columns named HessagelD (int) and MessageRaw (nvarchar(iux)).
MessageRaw can contain a phone number in multiple formats. and some rows do NOT contain a phone number. You need to write a single SELECT query that meets the following requirements:
* The query must return Message ID, RawNumber. DigitsOnly, and PhoneStatus.
* RawNumber must contain the first substring that matches a phone-number pattern, or NULL if no match exists.
* DigitsOnly must remove all non-digit characters from RawNumber. or return NULL.
* PhoneStatus must return Valid when a phone number exists in MessageRaw. otherwise return Missing.
How should you complete the Transact-SQL query? lo 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:
Explanation:
The correct drag-and-drop mapping is based on the documented behavior of the new SQL regular expression functions.
For RawNumber , the requirement is to return the first substring in MessageRaw that matches the phone- number pattern, or NULL if nothing matches. That is exactly what REGEXP_SUBSTR does: it extracts the matched substring from the source text. Microsoft documents REGEXP_SUBSTR as the function that
"extracts parts of a string based on a regular expression pattern" and returns the matched occurrence.
For DigitsOnly , you first need the matched phone substring, then remove all non-digit characters from it. The correct combined expression is REGEXP_REPLACE( REGEXP_SUBSTR( so the matched substring is passed into REGEXP_REPLACE, which strips characters matching \D. Microsoft documents REGEXP_REPLACE as returning a modified source string with matching patterns replaced. Using it around REGEXP_SUBSTR satisfies the "digits only or NULL" requirement in one select expression.
For PhoneStatus , the requirement is simply to return Valid when a phone number exists and Missing otherwise. That is a Boolean test, so REGEXP_LIKE is the right function. Microsoft documents REGEXP_LIKE as returning a Boolean value indicating whether the input matches the regex pattern.
NEW QUESTION # 15
You have an Azure SQL database that contains database-level Data Definition Language (DDL) triggers, including a trigger named ddl_Audit.
You need to prevent ddl_Audit from firing during the next deployment. The trigger object must remain in place.
Which Transact-SQL statement should you use?
Answer: E
Explanation:
The DISABLE TRIGGER Transact-SQL statement is the correct and appropriate solution for this scenario.
Solution Breakdown
To prevent a specific database-level DDL trigger from firing without removing the object, you can use the following syntax:
DISABLE TRIGGER [TriggerName] ON DATABASE;
Key Considerations
Object Retention: A disabled trigger remains in the database as an object and is visible in catalog views like sys.triggers, but it will not execute when its programmed events occur.
Reactivation: You can re-enable the trigger after your deployment is complete using the ENABLE TRIGGER statement.
Permissions: To execute this command on a database-scoped DDL trigger in Azure SQL, you must have at least ALTER ANY DATABASE DDL TRIGGER permission.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/statements/disable-trigger-transact-sql
NEW QUESTION # 16
......
With our DP-800 study materials, only should you take about 20 - 30 hours to preparation can you attend the exam. The rest of the time you can do anything you want to do to, which can fully reduce your review pressure. Saving time and improving efficiency is the consistent purpose of our DP-800 Learning Materials. With the help of our DP-800 exam questions, your review process will no longer be full of pressure and anxiety.
Exam DP-800 Score: https://www.examcost.com/DP-800-practice-exam.html
2026 Latest ExamCost DP-800 PDF Dumps and DP-800 Exam Engine Free Share: https://drive.google.com/open?id=1jeDYy433z_UOP84HTCv0FzwYwECfCLa8