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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Developing AI-Enabled Database Solutions (DP-800) |
| Exam Number: | DP-800 |
| Available Languages: | English |
| Passing Score: | 700/1000 (typical Microsoft passing score; subject to confirmation) |
| Exam Format: | Case studies, Multiple choice |
| Certificate Validity Period: | 1 year (Microsoft role-based certifications require annual renewal) |
| 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 # 19
You have a Microsoft SQL Server 2025 instance that contains a database named SalesDB SalesDB supports a Retrieval Augmented Generation (RAG) pattern for internal support tickets. The SQL Server instance runs without any outbound network connectivity.
You plan to generate embeddings inside the SQL Server instance and store them in a table for vector similarity queries.
You need to ensure that only a database user account named AlApplicationUser can run embedding generation by using the model.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Because the SQL Server 2025 instance has no outbound network connectivity , the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft's CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths . That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.
The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft's AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions . Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.
NEW QUESTION # 20
You have an Azure SQL database that contains tables named dbo.ProductDocs and dbo.ProductDocsEmbeddings. dbo.ProductDocs contains product documentation and the following columns:
- DocID (int)
- Title (nvarchar(200))
- Body (nvarchar(max))
- LastModified (datetime2)
The documentation is edited throughout the day.
dbo.ProductDocsEmbeddings contains the following columns:
- DocID (int)
- ChunkOrder (int)
- ChunkText (nvarchar(max))
- Embedding (vector(1536))
The current embedding pipeline runs once per night.
You need to ensure that embeddings are updated every time the underlying documentation content changes. The solution must NOT require a nightly batch process.
What should you include in the solution?
Answer: C
Explanation:
To ensure embeddings are updated immediately when the product information table changes, you should implement Azure SQL Database Change Tracking combined with Azure Functions triggered via Event Grid. This approach eliminates daily batch processes by enabling an event- driven architecture that triggers embedding regeneration only for modified rows.
Reference:
https://medium.com/@granthgharewal/why-change-tracking-needs-to-be-enabled-for-sql-trigger- azure-functions-7d230a669fb8
NEW QUESTION # 21
You have an Azure SQL database named AdventureWorksDB that contains a table named dbo.Employee.
You have a C# Azure Functions app that uses an HTTP-triggered function with an Azure SQL input binding to query dbo.Employee.
You are adding a second function that will react to row changes in dbo.Employee and write structured logs.
You need to configure AdventureWorksDB and the app to meet the following requirements:
* Changes to dbo.Employee must trigger the new function within five seconds.
* Each invocation must processes no more than 100 changes.
Which two database configurations should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: E,F
Explanation:
Azure Functions' Azure SQL trigger requires change tracking to be enabled on the source table. Microsoft' s SQL trigger documentation states that setting up change tracking for the Azure SQL trigger requires two steps : enable change tracking on the database and enable change tracking on the table being monitored.
Since the question asks specifically which database configurations you should perform, enabling change tracking on dbo.Employee is one of the required database-side steps.
To meet the latency requirement that changes trigger the function within five seconds , the relevant trigger setting is Sql_Trigger_PollingIntervalMs . Microsoft documents this setting as the delay, in milliseconds, between processing each batch of changes, and a value of 5000 means the trigger polls every 5 seconds .
A few clarifications about the other options:
* B is not the documented setting name. The documented app setting is Sql_Trigger_BatchSize or host setting MaxBatchSize , not "SetSql Trigger MaxBatchSize". The screenshot wording suggests a distractor.
* D is also required in practice for the trigger to work, but the question asks for two answers and includes the polling setting plus the table-level CT setting as the actionable choices presented.
* F is wrong because the Azure SQL trigger uses change tracking , not CDC.
NEW QUESTION # 22
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:
Explanation:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.
NEW QUESTION # 23
You need to meet the development requirements for the FeedbackJson column How should you complete the Transact SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText
CONTAINS(FeedbackJson, @Keyword)
SimilarityScore
These three selections are the correct way to complete the query because they align exactly with the stated requirements for the FeedbackJson column.
First, to extract the customer feedback text from the JSON document , the correct expression is JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText . Microsoft documents that JSON_VALUE is used to extract a scalar value from JSON, while JSON_QUERY is used for returning an object or array .
Since $.text is the textual feedback string, JSON_VALUE is the correct function.
Second, to filter rows where the JSON text contains a keyword , the best choice is CONTAINS (FeedbackJson, @Keyword) . The scenario explicitly states that FeedbackJson already has a full-text index
, and Microsoft documents that CONTAINS is the full-text predicate used in the WHERE clause to search full-text indexed character data. That makes it more appropriate than using EDIT_DISTANCE for keyword filtering.
Third, to order the results by similarity score, highest first , the correct item is SimilarityScore in the ORDER BY clause, which would be paired with DESC in the query. This matches the requirement to sort by the computed fuzzy similarity value. The DP-800 study guide specifically includes writing queries that use fuzzy string matching functions such as EDIT_DISTANCE, which supports the earlier computed SimilarityScore expression in the query.
NEW QUESTION # 24
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