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Microsoft Developing AI-Enabled Database Solutions 認定 DP-800 試験問題 (Q60-Q65):

質問 # 60
You need to create a table in the database to store the telemetry data. You have the following Transact-SQL code.

正解：

解説：

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.

質問 # 61
You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536).
You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3-smalL Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?

A. Convert the Embedding column to nvacchar(mex).
B. Regenerate embeddings for the existing rows.
C. Create a vector index on dbo.Products.Embedding.
D. Normalize the vector lengths before storing new embeddings.

正解：B

解説：
When you change embedding models, the stored vectors should be treated as belonging to a different embedding space unless you intentionally keep the entire corpus consistent. Microsoft's vector guidance notes that when most or all embeddings are replaced with fresh embeddings from a new model, the recommended practice is to reload the new embeddings and, for large-scale replacement scenarios, consider dropping and recreating the vector index afterward so search quality remains predictable.
This question also says applications must continue to use VECTOR_SEARCH without runtime errors .
VECTOR_SEARCH requires compatible vector dimensions, and the vector column already exists. Azure OpenAI documentation shows that text-embedding-ada-002 is fixed at 1536 dimensions and text- embedding-3-small supports up to 1536 dimensions . That means the migration can remain compatible with a VECTOR(1536) column, but the right implementation step is still to re-embed the existing rows so the table does not contain a mixed corpus produced by different models.
The other options are not appropriate:
* B normalization does not solve a model migration problem.
* C converting the vector column to nvarchar(max) would break vector-native search design.
* D a vector index improves performance, but it does not migrate old embeddings to the new model.

質問 # 62
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.

A. Create an AFTER trigger on dbo.Employee for Data Manipulation Language (DML).
B. Enable change tracking on the dbo. Employee table.
C. SetSql Trigger MaxBatchSize to 100.
D. Set Sql_Trigger_PollingIntervalMs to 5000.
E. Enable change data capture (CDC) for dbo.Employee table changes
F. Enable change tracking at the database level.

正解：B、D

解説：
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.

質問 # 63
You have an Azure SQL database that contains a table named knowledgebase, knowledgebase stores human resources (HR) policy documents and contains columns named title, content, category, and embedding.
You have an application named App1. App1 queries two relational tables named employee_pnofiles and benefits_enrollnent that contain HR data. App1 hosts a chatbot that calls a large language model (LLM) directly.
Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change. The chatbot also fails to answer questions that reference internal policy documents by title or category.
You need to recommend a Retrieval Augmented Generation (RAG) solution to resolve the chatbot issues.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:

The correct recommendation is to retrieve grounding data from knowledge_base and, at inference time, generate query embeddings and run a vector similarity search .
The chatbot currently answers some general HR questions but fails when policies change and when users ask about internal policy documents by title or category . That is exactly the kind of problem RAG is meant to solve: ground the LLM in the organization's proprietary content instead of relying on the model's training data or unrelated transactional tables. Microsoft's RAG guidance states that RAG extends LLMs by grounding responses in your own content and that, for agentic retrieval, knowledge bases unify knowledge sources for retrieval.
So the grounding data should come from knowledge_base , because that table stores the HR policy documents and already includes fields like title, content, category, and embedding. Those are the fields directly tied to the missing and outdated policy answers. By contrast:
* employee_profiles and benefits_enrollment are operational HR tables, not the authoritative store for policy-document grounding.
* PDF exports of the policies would be inferior to querying the indexed/structured knowledge base already prepared for retrieval.
* The LLM training data is specifically the wrong source when the issue is outdated internal content.
For the retrieval step, Microsoft's guidance says to use embeddings for vector queries and notes that vector similarity search matches concepts, not exact terms . This is especially important because users ask about policy documents by title or category and also phrase questions in ways that might not exactly match document wording. Generating a query embedding and then running a vector similarity search is the appropriate retrieval step in a RAG pipeline.

質問 # 64
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.

正解：

解説：

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.

質問 # 65
......

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