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| Section | Objectives |
|---|---|
| Design and implement data solutions | - Implement data storage and data processing solutions - Design database solutions using Azure data services |
| Integrate AI capabilities with database systems | - Implement AI-assisted data processing - Use Azure AI services with database workloads |
| Monitor, troubleshoot, and maintain solutions | - Troubleshooting data pipeline issues - Monitoring database health and performance |
| Develop and manage database solutions | - Ensure security and compliance of data solutions - Optimize performance and scalability |
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NEW QUESTION # 81
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Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change.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.
Answer:
Explanation:
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.
NEW QUESTION # 82
You have an Azure SQL database named SalesDB and an Azure App Service app named sales- api. SalesDB contains a table named dbo.Customers. dbo.Customers contains two columns named CreditCardNumber and TenantId.
Currently, sales-api connects to SalesDB by using SQL authentication with stored username and password.
You need to recommend a solution that meets the following requirements:
- Provides a passwordless method for sales-api to access SalesDB.
- Ensures that credit card numbers are NOT stored as plain text.
What should you include in the recommendation?
Answer: C
Explanation:
To move from SQL authentication to a more secure, passwordless architecture while protecting credit card data, you should implement Azure Managed Identities for authentication and Always Encrypted for data protection.
1. Enable Passwordless Authentication
Replace your stored username and password with a Managed Identity. This allows the App Service to authenticate with the database using its own identity, managed by Azure.
Step 1A: Enable Identity on App Service
Step 1B: Grant Database Access
Set an Entra ID (Active Directory) Admin for your SQL Server.
Connect to your database as that admin and run the following to create a user for your app:
Step 1C: Update Connection String
2. Protect Credit Card Numbers
Use Always Encrypted to ensure credit card numbers are encrypted before they even reach the database. The database engine never sees the plain text.
Step 2A: Store Encryption Keys
Step 2B: Encrypt the Column
Use the Always Encrypted Wizard in SQL Server Management Studio (SSMS).
Select the credit card column and choose Randomized encryption for maximum security (unless you need to search by exact matches, in which case use Deterministic).
Reference:
https://learn.microsoft.com/en-us/sql/relational-databases/security/encryption/always-encrypted- database-engine
NEW QUESTION # 83
You have an Azure container app named app1-contoso-001 that hosts a Data API builder (DAB) container in front of an Azure SQL database.
You add an entity named Todo that uses a source named dbo.todos.
Which URL pattern should clients use to access the Todo REST endpoint?
Answer: A
Explanation:
To access the REST endpoint for the Todo entity, clients should use the following URL pattern:
https://<your-container-app-url>/api/Todo
Base URL: The unique FQDN of your Azure Container App.
Path Prefix: By default, DAB uses /api for RESTful services.
Entity Name: You must use the Entity Name defined in the configuration (Todo), not the underlying source name (dbo.Todos).
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/connect-apps
NEW QUESTION # 84
You have an Azure SQL database that contains tables named dbo.ProduetDocs and dbo.
ProductuocsEnbeddings. dbo.ProductOocs contains product documentation and the following columns:
* Docld (int)
* Title (nvdrchdr(200))
* Body (nvarthar(max))
* LastHodified (datetime2)
The documentation is edited throughout the day. dbo.ProductDocsEabeddings contains the following columns:
* Dotid (int)
* ChunkOrder (int)
* ChunkText (nvarchar(aax))
* Embedding (vector(1536))
The current embedding pipeline runs once per night
Vou need to ensure that embeddings are updated every time the underlying documentation content changes The solution must NOT ' equire a nightly batch process.
What should you include in the solution?
Answer: A
Explanation:
The requirement is to ensure embeddings are updated every time the underlying content changes without relying on a nightly batch job. The right design is to enable change tracking on the source table so an external process can identify which rows changed and regenerate embeddings only for those rows. Microsoft documents that change detection mechanisms are used to pick up new and updated rows incrementally , which is the right pattern when you need near-continuous refresh instead of full nightly rebuilds.
This is better than:
* A. fixed-size chunking , which affects chunk strategy but not change detection.
* B. a smaller embedding model , which affects model cost/latency but not update triggering.
* C. table triggers , which would push embedding-maintenance logic directly into write operations and is generally not the best design for AI-processing pipelines. The question specifically asks for a solution that replaces the nightly batch requirement, not one that performs heavyweight work inline during every transaction.
NEW QUESTION # 85
Hotspot Question
You have a SQL database in Microsoft Fabric that uses the default settings for a newly created database and contains a table named Sales.Orders.
You have an application that uses two stored procedures to access Sales.Orders.
While monitoring database activity, you discover the following:
- sys.dm_exec_requests shows multiple sessions in a suspended state
with wait_type = LCK_M_X. All the sessions show the same wait_resource, which maps to Sales.Orders, and the same nonzero blocking_session_id.
- sys.dm_exec_input_buffer(blocking_session_id, NULL) returns a last
submitted command of BEGIN TRANSACTION UPDATE Sales.Orders.
- sys.dm_exec_sessions for the blocking session shows status = sleeping and open_transaction_count = 1.
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 # 86
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