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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q108-Q113):

NEW QUESTION # 108
Hotspot Question
You have an Azure SQL database that contains a table named Sales.Customer. Sales.Customer contains columns named CustomerId, FullName, Email, TaxID, and RegionId.
You have a database role named AppSupport that is used by a support application.
You need to implement a security solution for AppSupport that meets the following requirements:
- AppSupport must be prevented from viewing TaxID.
- AppSupport must be able to query Sales.Customer to troubleshoot
issues.
- AppSupport must be able to run a stored procedure named
Sales.usp_GetCustomerByCustomerId.
Which Transact-SQL statements should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 109
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Users report that the chatbot answers general HR questions correctly but provides outdated or incorrect answers when policies change.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 # 110
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.
Hotspot Question
You need to create a table in the database to store the telemetry data.
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:


NEW QUESTION # 111
You have an Azure SQL database.
You need to create a scalar user-defined function (UDF) that returns the number of whole years between an input parameter named @OrderDate and the current date/time as a single positive integer. The function must be created in Azure SQL Database.
You write the following code.

What should you insert at line 05?

Answer: B

Explanation:
Use RETURN to produce the scalar value of the function.
In an Azure SQL Database scalar function (a user-defined function that returns a single value), you must use the RETURN statement to return the scalar value.
The RETURN statement immediately terminates the function's execution and returns the value specified in its argument to the calling statement or procedure. The value returned must be of the data type specified in the RETURNS clause of the function definition.
The second argument to DATEDIFF should be @OrderDate as it is the start date, while the third argument is the end date, which is the current date.
Note:
DATEDIFF (Transact-SQL)
This function returns the count (as a signed integer value) of the specified datepart boundaries crossed between the specified startdate and enddate.
Syntax
DATEDIFF ( datepart , startdate , enddate )
Arguments
datepart
Specifies the units in which DATEDIFF reports the difference between the startdate and enddate.
Commonly used datepart units include month or second.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/functions/datediff-transact-sql


NEW QUESTION # 112
What is the role of Azure Synapse in AI + SQL solutions?

Answer: C

Explanation:
Azure Synapse Analytics combines big data and data warehousing for AI-driven insights.


NEW QUESTION # 113
......

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