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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Developing AI-Enabled Database Solutions (DP-800) |
| Exam Number: | DP-800 |
| Passing Score: | 700/1000 (typical Microsoft passing score; subject to confirmation) |
| Available Languages: | English |
| Certificate Validity Period: | 1 year (Microsoft role-based certifications require annual renewal) |
| Exam Format: | Multiple choice, Case studies |
| Recommended Training: | Microsoft Learn - Azure Data & AI Learning Paths |
| Exam Registration: | Microsoft Certification Dashboard |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No formal prerequisites required, but familiarity with Azure data services and database concepts is recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/ |
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NEW QUESTION # 44
Which service enables natural language querying over SQL data?
Answer: A
Explanation:
Azure OpenAI Service allows users to query structured SQL data using natural language via LLMs.
NEW QUESTION # 45
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 to resolve the slow dashboard query issue. What should you recommend?
Answer: B
Explanation:
Scenario:
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.
To optimize this query, you should create a covering nonclustered index that handles both the filtering (WHERE) and the sorting (ORDER BY) requirements while including the remaining columns to avoid expensive lookups.
Recommended Index Strategy
Creating an index with FleetID as the first key column and LastUpdateUtc as the second key column will allow SQL Server to perform an Index Seek to find the specific fleet and then retrieve those rows in the pre-sorted order required by your ORDER BY clause.
T-SQL Implementation:
CREATE NONCLUSTERED INDEX IX_VehicleHealth_Fleet_Update
ON dbo.VehicleHealthSummary (FleetID, LastUpdateUtc DESC)
INCLUDE (EngineStatus, BatteryHealth);
Use code with caution.
Why this works
Eliminates Clustered Index Scan: The current plan scans the entire table because no index exists that starts with FleetID. This new index allows the engine to jump directly to the relevant rows.
Avoids a Sort Operator: By including LastUpdateUtc DESC in the index key, the data is already physically ordered. SQL Server can read the index and return the results immediately without needing a costly in-memory sort.
Fully Covers the Query: Using the INCLUDE clause for EngineStatus and BatteryHealth ensures all data required by the SELECT statement is present in the index. This prevents "Key Lookups," where the engine would otherwise have to go back to the original table for those specific values.
Reference:
https://www.mssqltips.com/sqlservertip/8192/sql-server-uses-non-clustered-index-rather-than- clustered-index
NEW QUESTION # 46
You have an Azure subscription. The subscription contains an Azure SQL database named SalesDB and an Azure App Service app named sales-api. sales-api uses virtual network integration to a subnet named vnet- prod/subnet-app and reads from SalesDB You need to configure authentication and network access to meet the following requirements:
* Ensure that sales-api connects to SalesDB by using passwordless authentication.
* Ensure that all the database traffic remains within the subscription.
The solution must minimize administrative effort
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For authentication , the correct choice is Enable a managed identity and use Microsoft Entra authentication . Azure App Service supports managed identity, and Microsoft documents this as the recommended way to connect to Azure SQL Database without passwords or secrets . This minimizes administrative effort because there are no SQL passwords to rotate, store, or manage.
For network access , the correct choice is Create a private endpoint and disable public network access .
Microsoft documents that when public network access is disabled , Azure SQL Database only accepts connections through private endpoints . That keeps the database traffic on Azure private networking rather than exposing it through the public endpoint. Microsoft also notes that simply adding a private endpoint does not disable public access by itself, so disabling public network access is part of the correct configuration.
The other options are weaker:
* Rotated SQL credentials or storing SQL logins in Key Vault still use passwords and create more admin overhead.
* Allowing Azure services, adding outbound IP firewall rules, or database firewall rules still rely on the public endpoint , so they do not best satisfy the requirement that database traffic remain within the subscription.
NEW QUESTION # 47
You have an Azure SQL database named SalesDB that contains a table named dbo. Articles, dbo.Articles contains two million articles with embeddmgs. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARQi
Users report that semantic search results do NOT reflect the updates until the following day.
Vou need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB Which embedding maintenance method should you implement?
Answer: B
Explanation:
The correct answer is B because the problem is not the vector search operator itself. The problem is that embeddings are becoming stale when article content changes . Microsoft documents that change data capture (CDC) tracks insert, update, and delete operations on source tables, which makes it the right mechanism to identify only the rows that changed.
This also best satisfies the requirement to minimize CPU usage on SalesDB . With CDC, the database only records the row changes, and the embedding regeneration work can be moved to an external process such as an Azure Functions app. That avoids running embedding generation inline inside the database for every update and avoids repeatedly recalculating embeddings for unchanged rows. In contrast, an hourly full-table regeneration would be extremely wasteful on a table with two million frequently updated articles, and a trigger that calls embedding generation per row would push expensive AI work into the transactional path of the database.
Option A is incorrect because changing from VECTOR_SEARCH to VECTOR_DISTANCE does not regenerate embeddings; it only changes the retrieval method. Microsoft states that VECTOR_SEARCH is the ANN search function, while VECTOR_DISTANCE performs exact distance calculation, so neither option addresses stale embedding data.
So the right design is:
* use CDC to detect only changed articles,
* process those changes outside the database,
* regenerate embeddings only for changed rows,
* write back the refreshed embeddings for current semantic search results.
NEW QUESTION # 48
You have a SQL database in Microsoft Fabric that contains a table named dbo.Products. dbo.Products contains product catalog data.
You need to create a stored procedure that performs hybrid search. The solution must meet the following requirements:
* Use approximate nearest neighbor (ANN) to retrieve the top 20 candidate products.
* Re-rank only the candidates that also match a full-text query.
* Generate the query embedding.
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:
NEW QUESTION # 49
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