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| Section | Objectives |
|---|---|
| Integrate AI capabilities with database systems | - Use Azure AI services with database workloads - Implement AI-assisted data processing |
| Develop and manage database solutions | - Ensure security and compliance of data solutions - Optimize performance and scalability |
| Monitor, troubleshoot, and maintain solutions | - Troubleshooting data pipeline issues - Monitoring database health and performance |
| Design and implement data solutions | - Implement data storage and data processing solutions - Design database solutions using Azure data services |
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37. Frage
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets What should you include in the workflow?
Antwort: B
Begründung:
The correct workflow step is Option C because it uses the Azure SQL GitHub Action to publish a .dacpac file and reads the connection string from GitHub repository secrets, which is exactly what the requirement asks for. Microsoft's Azure SQL GitHub Actions guidance shows using azure/sql-action@v2 with a connection string stored in secrets and a DACPAC path for deployment.
The key parts that make C correct are:
uses: azure/sql-action@v2
action: publish
path: bin/Debug/db1.dacpac
connection-string: ${{ secrets.SQL_CONNECTION_STRING }}
That matches the documented publish pattern for deploying a DACPAC to Azure SQL Database from GitHub Actions. Microsoft and the Azure SQL action documentation both describe Publish as the deployment action for applying a DACPAC to a target database, while Extract is used to create a DACPAC from an existing database, not deploy one.
Why the other options are incorrect:
A uses an environment variable defined inline with a visible connection string rather than using GitHub repository secrets, which does not meet the requirement.
B uses action: extract, which would create a DACPAC from a database instead of publishing the existing DACPAC artifact.
D passes a target connection string to dotnet build, but the question says the workflow already runs dotnet build and produces a database artifact. The missing step is the deployment/publish step, not another build step. Microsoft's SQL project automation guidance separates build the DACPAC from publish the DACPAC.
38. Frage
You have an Azure SQL database that contains a table named Customer. Customer contains the following columns:
NationalIDNumber is unique per customer.
InvestigationNotes contains free-form text.
You have a stored procedure that performs point lookups by NationalIDNumber and returns InvestigationNotes in the query results without filtering on InvestigationNotes.
You need to encrypt both columns by using Always Encrypted and the highest security possible.
Which type of Always Encrypted encryption should you use for each column?
Antwort: B
39. Frage
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets What should you include in the workflow?
Antwort: B
Begründung:
The correct workflow step is Option C because it uses the Azure SQL GitHub Action to publish a .dacpac file and reads the connection string from GitHub repository secrets , which is exactly what the requirement asks for. Microsoft's Azure SQL GitHub Actions guidance shows using azure/sql-action@v2 with a connection string stored in secrets and a DACPAC path for deployment.
The key parts that make C correct are:
* uses: azure/sql-action@v2
* action: publish
* path: bin/Debug/db1.dacpac
* connection-string: ${{ secrets.SQL_CONNECTION_STRING }}
That matches the documented publish pattern for deploying a DACPAC to Azure SQL Database from GitHub Actions. Microsoft and the Azure SQL action documentation both describe Publish as the deployment action for applying a DACPAC to a target database, while Extract is used to create a DACPAC from an existing database, not deploy one.
Why the other options are incorrect:
* A uses an environment variable defined inline with a visible connection string rather than using GitHub repository secrets , which does not meet the requirement.
* B uses action: extract, which would create a DACPAC from a database instead of publishing the existing DACPAC artifact.
* D passes a target connection string to dotnet build, but the question says the workflow already runs dotnet build and produces a database artifact . The missing step is the deployment/publish step, not another build step. Microsoft's SQL project automation guidance separates build the DACPAC from publish the DACPAC .
40. Frage
You have a Microsoft SQL Servei 2025 database that contains a table named dbo.Customer-Messages, dbo.
Customer-Messages contains two columns named HessagelD (int) and MessageRaw (nvarchar(iux)).
MessageRaw can contain a phone number in multiple formats. and some rows do NOT contain a phone number. You need to write a single SELECT query that meets the following requirements:
* The query must return Message ID, RawNumber. DigitsOnly, and PhoneStatus.
* RawNumber must contain the first substring that matches a phone-number pattern, or NULL if no match exists.
* DigitsOnly must remove all non-digit characters from RawNumber. or return NULL.
* PhoneStatus must return Valid when a phone number exists in MessageRaw. otherwise return Missing.
How should you complete the Transact-SQL query? lo answer, drag the appropriate values To the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The correct drag-and-drop mapping is based on the documented behavior of the new SQL regular expression functions.
For RawNumber, the requirement is to return the first substring in MessageRaw that matches the phone- number pattern, or NULL if nothing matches. That is exactly what REGEXP_SUBSTR does: it extracts the matched substring from the source text. Microsoft documents REGEXP_SUBSTR as the function that
"extracts parts of a string based on a regular expression pattern" and returns the matched occurrence.
For DigitsOnly, you first need the matched phone substring, then remove all non-digit characters from it. The correct combined expression is REGEXP_REPLACE( REGEXP_SUBSTR( so the matched substring is passed into REGEXP_REPLACE, which strips characters matching \D. Microsoft documents REGEXP_REPLACE as returning a modified source string with matching patterns replaced. Using it around REGEXP_SUBSTR satisfies the "digits only or NULL" requirement in one select expression.
For PhoneStatus, the requirement is simply to return Valid when a phone number exists and Missing otherwise. That is a Boolean test, so REGEXP_LIKE is the right function. Microsoft documents REGEXP_LIKE as returning a Boolean value indicating whether the input matches the regex pattern.
41. Frage
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 generate embeddings to resolve the issues identified by the analysts. Which column should you use?
Antwort: A
Begründung:
Scenario: VehicleIncidentReports often contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
The column you should use to generate embeddings is IncidentDescription.
Why this is the correct choice:
Semantic Meaning: Embeddings are designed to capture the semantic meaning of rich, unstructured text. The IncidentDescription column likely contains the detailed natural language notes about weather, traffic, and location that the analysts are struggling to search effectively using standard methods.
Vector Search: By converting the text in IncidentDescription into mathematical vectors (embeddings), you can perform a semantic vector search. This allows the database to find
"similar" incidents based on the context of the description rather than just matching exact keywords or categories.
Incorrect:
[Not A]
VehicleLocation: Typically contains specific coordinates or addresses which are better handled by spatial data types or exact matching.
[Not C]
IncidentType: Is likely a categorical label (e.g., "Fender Bender", "Multi-car pileup") and too simple for meaningful embedding generation.
[Not D]
SeverityScore: Is a numeric value that represents a magnitude, not a semantic concept.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/spatial-geography/spatial-types-geography
42. Frage
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