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| Section | Objectives |
|---|---|
| Develop and manage database solutions | - Ensure security and compliance of data solutions - Optimize performance and scalability |
| 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 | - Use Azure AI services with database workloads - Implement AI-assisted data processing |
| Monitor, troubleshoot, and maintain solutions | - Troubleshooting data pipeline issues - Monitoring database health and performance |
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質問 # 74
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output.
Which should you use?
正解：A
解説：
To protect sensitive Azure SQL database columns so that neither stored values nor query inputs reveal actual data--and to prevent inference of relationships/repetitions--Always Encrypted with Randomized Encryption is recommended. This approach ensures that data is encrypted on the client side, and the database engine never handles plaintext data or keys.
Key Recommendations:
Always Encrypted (Randomized): Use randomized encryption, which produces different encrypted values (ciphertext) for the same plaintext, preventing pattern analysis.
Azure Key Vault: Store column encryption keys in Azure Key Vault for secure, manageable key storage.
Client-side Encryption: Data is encrypted/decrypted within the application's memory using the SQL client driver, ensuring the database engine or high-privileged users cannot access the data.
Incorrect:
[Not D]
Deterministic Encryption: Allows matching queries (e.g., WHERE Column = 'Value'), but allows attackers to infer patterns and repetitions in data.
Transparent Data Encryption (TDE): Protects data at rest, but SQL users can still see plaintext data, violating the requirement.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-configure-always-encrypted-in-azure-sql- database/view
質問 # 75
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output Which should you use?
正解：A
解説：
The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext , which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.
That makes randomized encryption the right choice here:
* It protects data at rest and in transit/query parameters under Always Encrypted's client-side encryption model.
* It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.
Why the other options are wrong:
* A. Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption .
* C. RLS controls row access, not value confidentiality.
* D. Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.
質問 # 76
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 recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements. What should you include in the recommendation?
正解：B
解説：
Scenario: Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
To retrieve live metadata from Azure SQL databases and use it with GitHub Copilot in Visual Studio Code (VS Code), you must use the SQL Server (mssql) extension. This extension provides the native capability to extract a database schema as a .dacpac file directly within the editor.
1. Export the Schema as a .dacpac File
You can extract the schema of your live Azure SQL database using the SQL Server (mssql) extension.
2. Load the .dacpac into GitHub Copilot Context
Once the .dacpac file is saved in your VS Code workspace, you can provide it as context to GitHub Copilot Chat using #-mentions or Drag & Drop.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sql-database-projects/concepts/data-tier- applications/extract-dacpac-from-database
質問 # 77
You have an Azure SQL database that supports an OLTP application.
You need to write Transact-SQL code that returns blocking chain details. The output must return only sessions that ate blocked or are blocking other sessions.
How should you complete the code? To 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.
正解：
解説：
Explanation:
* CTE inner source # FROM sys.dm_exec_requests
* Join after sys.dm_exec_sessions AS s # LEFT OUTER JOIN sys.dm_exec_requests
* Text retrieval # OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)
* Input buffer retrieval # OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id) The correct drag-and-drop choices are based on how blocking-chain details are normally assembled in Azure SQL Database.
The CTE must read from sys.dm_exec_requests because the alias er is used with er.session_id and er.
blocking_session_id, and those columns come from sys.dm_exec_requests. Microsoft documents that sys.
dm_exec_requests returns information about executing requests and includes the blocking_session_id column used to identify blockers.
After FROM sys.dm_exec_sessions AS s, the correct join is LEFT OUTER JOIN sys.dm_exec_requests so the query can still return sessions from sys.dm_exec_sessions even when a current request row is missing.
This is useful when showing sessions that are blocked or blocking, while still attempting to attach current request details when available.
For batch text, use OUTER APPLY sys.dm_exec_sql_text(r.sql_handle) because Microsoft documents sys.
dm_exec_sql_text(sql_handle) as the function that returns the SQL batch text for the specified sql_handle.
For the input buffer, use OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id) because Microsoft documents that sys.dm_exec_input_buffer takes session_id and request_id and returns event_info, which is commonly used when sys.dm_exec_sql_text is null or when you want the last command text.
So the completed code uses:
* FROM sys.dm_exec_requests
* LEFT OUTER JOIN sys.dm_exec_requests
* OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)
* OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id)
質問 # 78
You have an Azure SQL database named ProductsDB.
You deploy Data API builder (DAB) to Azure Container Apps by using the
mcr.microsoft.com/azure-databases /data-api-builder:latest image.
The container app has the following configurations:
- Secrets: mssql-connection-string, dab-config-base64
- Environment variables:
- MSSQL_CONNECTION_STRING=secretref:mssql-connection-string
- DAB_CONFIG_BASE64=secretref:dab-config-base64
- Ingress: External on port 5000
Users report that the /health endpoint returns a healthy response, but all requests that query an entity named Products fail and generate a connection error.
You confirm that the SQL login in the connection string is correct and the database exists.
You need to ensure that the container app can establish connections to the Azure SQL logical server without changing the container app deployment settings or the DAB configuration file.
What should you do on the Azure SQL logical server?
正解：C
解説：
Even if the login credentials are correct, Azure SQL Database blocks all incoming traffic by default. Since your Container App is returning a healthy response for the /health endpoint (which is internal to the DAB engine) but failing on entity queries (which require a database hit), the network handshake is being rejected at the SQL Server firewall level.
To fix this connection error without changing the container app or the DAB configuration file, you must enable the Azure SQL Server firewall to allow Azure services.
Setting the start and end IP address to 0.0.0.0 in an Azure SQL Database firewall rule is a specific configuration that enables the "Allow Azure services and resources to access this server" setting.
Primary Use Case
In your scenario, this rule allows your Azure Container Apps (ACA) to communicate with your Azure SQL Database over the Azure backbone network.
Connectivity: It permits any traffic originating from within the Azure boundary to reach the database.
Simplicity: You do not need to track or white-list the specific outbound IP addresses of your Container App, which can change if the app scales or restarts.
Internal Routing: Traffic stays within the Azure network rather than routing out to the public internet and back in.
Reference:
https://learn.microsoft.com/en-us/azure/azure-sql/database/firewall-configure
https://stackoverflow.com/questions/54599813/how-to-enable-the-access-to-azure-services-in- my-azure-sql-database-server
質問 # 79
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