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NEW QUESTION # 68
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.
Answer:
Explanation:
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.
NEW QUESTION # 69
You need to recommend a solution that will resolve the ingestion pipeline failure issues. Which two actions should you recommend? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads , and these two controls address those two problems directly. Microsoft's JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON , and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.
For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design.
Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index . That makes a persisted hash-based computed column plus a unique index a practical and exam- consistent way to reject duplicate payloads efficiently.
The other options do not solve the stated root causes:
* Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.
* A trigger to rewrite malformed JSON is not the right integrity control and is brittle.
* Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention
Topic 1, Contoso Case Study
Existing 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 FeedbackJson column has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the unmask permission.
Requirements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following;
* Al 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.
SELECT VehicleTd, Lastupdatedutc, EngineStatus, BatteryHealth FROM dbo.VehicleHealthSumary where fleetld - gFleetld ORDER BV LastUpdatedUtc DESC; You review the execution plan and discover that the plan shows a clustered index scan.
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.
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support Al-powered semantic search over incident reports.
Security 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 accuracy JSON 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 MaintenanceEvents table updates the corresponding value in the LastModif reduce column to the time of the change.
* Avoid recursive updates.
AI Search, Embedding's, and Vector indexing
The development learn 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 FeedbackJson column of the customer-Feedback table:
* 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.
NEW QUESTION # 70
Case Study 2 - Fabrikam
Existing Environment
Azure Environment
Fabrikam has a single Azure subscription in the East US 2 Azure region. The subscription contains an Azure SQL database named DB1. DB1 contains the following tables:
* Patients
* Employees
* Procedures
* Transactions
* UsefulPrompts
* ProcedureDocuments
You store a column master key as a secret in Azure Key Vault.
You have an on-premises application named TransactionProcessing that uses a hard-coded username and password in a connection string to access DB1.
Problem Statements
Users report that after executing a long-running stored procedure named sp_UpdateProcedureForPatient, updates to the underlying data are sometimes inconsistent.
Requirements
Planned Changes
Fabrikam plans to manage all changes to Azure SQL Database objects by using source control in GitHub. Every pull request submitted to production will be validated before it can be merged.
Deployments must use the Release configuration.
Security Requirements
Fabrikam identifies the following security requirements:
* The TransactionProcessing application must use a passwordless connection to DB1.
* The Employees table contains two columns named TaxID and Salary that must be encrypted at rest.
* Auditors must have a tamper-evident history of transactions with cryptographic proof of changes to the employee data.
Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
AI Search, Embeddings, and Vector Indexing
Fabrikam identifies the following AI-related requirements:
* Queries to the ProcedureDocuments table must use Reciprocal Rank Fusion (RRF).
* Users must be able to query the data in DB1 by using prompts in Copilot in Microsoft Fabric.
* The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
Development Requirements
Fabrikam identifies the following development requirements:
* Provide the functionality to retrieve all the transactions of a given patient between two dates, showing a running total.
* Expose a Data API builder (DAB) configuration file to enable Azure services to perform the following operations over a REST API:
- Read data from the procedures table without authentication.
- Read and insert data into the Transactions table once authenticated.
- Execute the sp_UpdateProcedurePatient stored procedure.
* Provide the functionality to retrieve a list of the names of patients who underwent medical procedures during the last 30 days.
* Information for each medical procedure will be stored in a table. The table will be used with a large language model (LLM) for user querying and will have the following structure.
DAB
You create a DAB configuration file that meets the development requirements for DB1 and includes the following entities.
You plan to implement changes to sp_UpdateProcedureForPatient to meet the performance requirements.
You change the stored procedure to run all its code within a transaction.
Which transaction level should you use?
Answer: B
Explanation:
Scenario: Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
To prevent other transactions from changing records while your stored procedure runs, you should use the Repeatable Read isolation level.
Repeatable ReadMechanism: Places shared locks on all data read by the query.
Prevention: Prevents other transactions from modifying or deleting the rows you have accessed.
Consistency: Ensures that if you read the same data twice within the procedure, the values remain identical.
Incorrect:
[Not C]
Serializable
Strictness: This is the highest level of isolation.
Phantom Protection: In addition to preventing changes to existing records, it prevents other transactions from inserting new records that would fall into the range of your query (phantom reads).
Usage: Use this if your procedure relies on aggregate counts or specific ranges that must remain absolutely static.
Key Considerations
Concurrency: Both levels reduce system performance because they hold locks longer, potentially causing other users to wait.
Deadlocks: Higher isolation levels increase the risk of deadlocks in busy databases.
Reference:
https://learn.microsoft.com/en-us/sql/connect/jdbc/understanding-isolation-levels
NEW QUESTION # 71
You need to create a table in the database to store the telemetry data. You have the following Transact-SQL code.
Answer:
Explanation:
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.
NEW QUESTION # 72
You need to design a generative AI solution that uses a Microsoft SQL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
- Are grounded in the latest transactional and reference data stored in DB1
- Do NOT require retraining or fine-tuning the language model when the
data changes
- Can include citations or references to the source data used in the
response
Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: B
Explanation:
Implementing a Retrieval-Augmented Generation (RAG) pattern with Microsoft SQL Server 2025 allows you to ground generative AI responses in real-time company data without the need for model fine-tuning. This version of SQL Server functions as a native vector database, enabling seamless integration between relational data and AI-powered insights.
Incorrect:
[Not B]
Training a custom language model is not a use case for the Retrieval Augmented Generation (RAG) pattern.
In fact, the primary purpose of RAG is to provide a language model with up-to-date information without the need for training, retraining, or fine-tuning.
Reference:
https://devblogs.microsoft.com/azure-sql/sql-server-2025-embraces-vectors-setting-the- foundation-for-empowering-your-data-with-ai
NEW QUESTION # 73
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