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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Exam Price: | $165 USD |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 (on a scale of 1-1000) |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Available Languages: | Spanish, Korean, English, Chinese (Simplified), Japanese, French, Portuguese (Brazil), German |
| Exam Format: | Active screen, Multiple select, Drag and drop, Case studies, Multiple choice |
| 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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| Topic | Details |
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| Topic 1 |
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| Topic 2 |
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| Topic 3 |
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NEW QUESTION # 64
You need to create a table in the database to store the telemetry data. You have the following Transact-SQL code.
Answer:
Explanation:
Explanation:
The first statement is No. The requirement says telemetry data must be stored in a partitioned table to provide predictable performance for ingestion and retention operations. However, the shown CREATE TABLE statement does not define a partition function or partition scheme, and the table is created with a regular clustered primary key on TelemetryId. Microsoft's partitioning guidance states that creating a partitioned table requires a partition function, a partition scheme, and creating the table or index on that partition scheme using a partitioning column. None of that appears in the code, so the table is not partitioned.
The second statement is Yes. The code creates a JSON index named JI_VehicleTelemetry_Location on LocationJson for these specific JSON paths: $.location.latitude, $.location.longitude, and $.location.accuracy.
That matches the requirement that those JSON properties must be filterable by using an index seek. Microsoft documents that JSON indexing is used to optimize filtering and sorting on JSON properties, and the index only helps for the properties included in the index definition.
The third statement is No. The JSON index is defined only for latitude, longitude, and accuracy. A query filtering on $.location.heading references a different path that is not included in the index definition, so that query would not use JI_VehicleTelemetry_Location for that predicate. JSON indexes are path-specific; they do not automatically cover unrelated properties in the same JSON document.
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 # 65
Which component handles security in Azure SQL?
Answer: A
Explanation:
Azure Key Vault securely stores secrets and keys.
NEW QUESTION # 66
You have a Microsoft SQL Server 2025 database that contains a table named products. products contains two columns named description and embedding. embedding is generated from description.
You have an application named App1. App1 has a feature that uses the following query.
VECTOR_DISTANCE('cosine', @query_vector, embedding)
Users report that the feature is slow during peak usage times.
You discover that during peak usage times, the semantic search latency increases and CPU utilization spikes.
You need to reduce latency and CPU utilization related to the App1 feature.
What should you do?
Answer: D
Explanation:
The correct answer is C because the current query uses VECTOR_DISTANCE( ' cosine ' , @query_vector, embedding) , which performs an exact vector distance calculation . Microsoft explicitly documents that VECTOR_DISTANCE does not use a vector index , even when one exists. Exact k-nearest-neighbor searches can require scanning large numbers of vectors and calculating distances row by row, which explains the observed CPU spikes and increased semantic-search latency during peak load.
A vector index on products.embedding is specifically designed to accelerate nearest-neighbor search, and VECTOR_SEARCH is the Transact-SQL function that can use that approximate nearest-neighbor index when the index metric matches the query metric. Using METRIC = ' cosine ' preserves the same similarity metric currently used by App1 while reducing the amount of computation required.
The other choices are incorrect:
* A normalizes vectors but does not replace the expensive exact search with indexed ANN retrieval.
* B replaces semantic vector search with lexical full-text search and changes the feature's behavior.
* D a standard nonclustered B-tree index cannot accelerate vector-distance operations.
* E converting embeddings to VARBINARY(8000) removes native vector semantics and does not solve the performance problem.
Therefore, the exam-aligned solution is create a vector index and use VECTOR_SEARCH with cosine distance .
NEW QUESTION # 67
You have an Azure SQL database named sqldb-ai-prod that stores customer support tickets for a multitenant software as a service (SaaS) application. sqldb-ai-prod contains a table named Tickets. Tickets contains columns named TenantId, TicketId, CustomerEmail, CustomerPhone, and Notes.
You plan to harden data access, since a new support team will use ad hoc reporting tools that connect directly to sqldb-ai-prod.
You need to configure security to meet the following requirements:
* Support agents must see only the rows of their own TenantId column.
* Support agents must see only the domain name portion of the CustomerEmail column.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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:
Verified Answer : =
* Support agents must see only the rows of their own TenantId # Create a row-level security (RLS) predicate that uses the user context.
* Support agents must see only the domain name portion of CustomerEmail # Configure dynamic data masking on selected columns.
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = For the TenantId requirement, use Row-Level Security (RLS) . RLS is designed to restrict which rows a database principal can access. A security predicate can compare the row ' s TenantId with tenant information derived from the current user ' s session or security context, so support agents querying the same Tickets table see only rows belonging to their own tenant. This is the correct multitenant isolation mechanism because it is enforced by the database rather than relying on individual reporting tools or application-side filtering.
For CustomerEmail , use Dynamic Data Masking (DDM) . DDM controls how sensitive column values are displayed to users who do not have UNMASK permission. A partial masking rule can conceal the local/user- specific portion of an email address while exposing only the required portion, such as its domain, according to the masking configuration.
The remaining choices do not satisfy these specific requirements. Transparent Data Encryption (TDE) protects database files at rest but does not filter rows or selectively obscure query results. Always Encrypted protects sensitive values cryptographically and is appropriate when the database engine itself should not see plaintext, but it is not the right mechanism when support agents must deliberately see a controlled portion of an email address.
Final drag-and-drop selections:
* Own TenantId rows # Create a row-level security (RLS) predicate that uses the user context
* Only domain portion of CustomerEmail # Configure dynamic data masking on selected columns
NEW QUESTION # 68
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 # 69
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