P.S. Free & New DP-800 dumps are available on Google Drive shared by Real4test: https://drive.google.com/open?id=1lfvdS7_h-xxyOCmIknL7jI95NVxRe8g5
We offer you free update for one year for DP-800 study guide, namely, in the following year, you can obtain the latest version for free. And the latest version for DP-800 exam dumps will be sent to your email automatically. In addition, DP-800 exam materials are high quality, since we have experienced experts to compile and verify them, therefore the quality and accuracy can be guaranteed, so you can use them at ease. We have online and offline chat service, and if you have any questions about DP-800 Exam Dumps, you can consult us, and we will give you reply as quickly as possible.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Available Languages: | German, Japanese, Spanish, French, English, Chinese (Simplified), Korean, Portuguese (Brazil) |
| Exam Duration: | 100 minutes |
| Exam Format: | Drag and drop, Case studies, Multiple choice, Active screen, Multiple select |
| Exam Price: | $165 USD |
| 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) |
| 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 |
>> Microsoft DP-800 Valid Exam Braindumps <<
Are you still worrying about the high difficulty to pass Microsoft certification DP-800 exam? Are you still sleeplessly endeavoring to review the book in order to pass Microsoft DP-800 Exam Certification? Do you want to pass Microsoft DP-800 exam certification faster? Be quick to select our Real4test! Having it can quickly fulfill your dreams.
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
NEW QUESTION # 14
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?
Answer: C
Explanation:
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
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: D
Explanation:
The requirement is very specific: prevent ddl_Audit from firing during the next deployment, but leave the trigger object in place. Microsoft documents DISABLE TRIGGER as the statement used to disable a trigger without dropping it. That is exactly the right operation for a temporary suspension of a DDL trigger. For a database-scoped DDL trigger, the syntax is on the database scope, for example DISABLE TRIGGER ddl_Audit ON DATABASE;.
The other options do not meet the requirement as directly:
ALTER TRIGGER changes the trigger definition, not simply disables execution.
ALTER DATABASE is not the direct statement for disabling a specific DDL trigger.
ALTER SERVER AUDIT SPECIFICATION and ALTER DATABASE AUDIT SPECIFICATION are audit- feature statements, not trigger-control statements.
So the correct Transact-SQL statement is DISABLE TRIGGER.
NEW QUESTION # 16
You have a database named DB1. The schema is stored in a GitHub repository as an SDK style SQL database project.
You use a feature branch workflow to deploy changes to DB1
You need to update the local feature branch with the latest changes to main, and then create a pull request to merge the feature branch into main for review.
How should you complete the GitHub CLI script? 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:
Explanation:
The correct sequence is:
* git fetch origin
* git merge origin/main
* gh pr create
This is the right workflow because the script starts on the local feature branch:
git checkout feature/db1-add-staticdata
To update that local feature branch with the latest changes from main, you first fetch the latest remote refs with git fetch origin , then merge the updated remote main branch into the current feature branch with git merge origin/main . After the feature branch is up to date, the correct GitHub CLI command to open the pull request is gh pr create . GitHub's CLI documentation shows that gh pr create is the command used to create a pull request, and supports flags such as --title, --body, --head, --base, --repo, and --web, which match the script shown in the question.
The other commands are not the best fit here:
* git checkout main would move you off the feature branch, which is not what you want before merging main into the feature branch.
* git pull origin main could update from remote main, but the script pattern here clearly separates fetching and then merging.
* gh pr merge merges an existing pull request, not create one.
* gh pr ready marks a draft PR as ready for review, but does not create the PR.
NEW QUESTION # 17
You have an Azure SQL database that supports an AI-enabled product catalog API. The database contains a table named Sales.Orders. Sales.Orders contains 20 million rows and has a nonclustered index on a column named CreateDate. The API passes a date parameter named @OrderDate. You have a stored procedure named get_latest_day_orders that filters by running the following query: WHERE CAST(CreateDate AS date) =
@OrderDate A review of the query execution reveals that the query causes an index scan. You need to modify the predicate to ensure that Microsoft SQL Server can use an index seek on CreateDate for a single day. Which WHERE clause should you use?
Answer: A
NEW QUESTION # 18
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.
Hotspot Question
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 19
......
DP-800 Related Content: https://www.real4test.com/DP-800_real-exam.html
2026 Latest Real4test DP-800 PDF Dumps and DP-800 Exam Engine Free Share: https://drive.google.com/open?id=1lfvdS7_h-xxyOCmIknL7jI95NVxRe8g5