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NEW QUESTION # 11
You have an Azure SQL database that supports a customer-facing API. The API calls a stored procedure named dbo.GetCustomerOrders thousands of times per hour.
After a deployment that updated indexes and statistics, users report that the API endpoint backed by dbo.
Getcustomerorders is slower. In Query Store, the same query now has two persisted execution plans. During the last hour, the newer plan had a significantly higher average duration and CPU time than the older plan.
You need to restore the previous performance quickly, without changing the API code.
Which Transact-SQL command should you run?
Answer: C
Explanation:
The scenario says Query Store already shows two persisted execution plans for the same query, and the older plan performed much better than the newer one during the last hour. Microsoft documents that sp_query_store_force_plan is used to force a particular plan for a particular query in Query Store .
That makes it the fastest way to restore the previously good plan without changing application code , which is exactly what the question requires.
Why the other options are not the best fit:
* sp_query_store_set_hints is for adding or updating Query Store hints to influence compilation behavior, but when you already know the exact older good plan, Microsoft points to plan forcing as the direct remedy.
* DBCC FREEPROCCACHE clears cached plans broadly and is disruptive; it does not guarantee a return to the known good plan.
* ALTER DATABASE is too general and does not directly restore the prior execution plan.
So the right Transact-SQL command is:
EXEC sp_query_store_force_plan
using the relevant @query_id and @plan_id from Query Store for the older, better-performing plan.
Microsoft also notes that when a plan is forced, SQL Server tries to use that plan whenever it encounters the query again.
NEW QUESTION # 12
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 # 13
Hotspot Question
You have an Azure SQL database that contains a table named stores. stores contains a column named description and a vector column named embedding.
You need to implement a hybrid search query that meets the following requirements:
- Uses full-text search on description for the keyword portion
- Returns the top 20 results based on a combined score that uses a
weighted formula of 60% vector distance and 40% full-text rank
How should you configure the query components? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 14
You have an Azure SQL database that contains a table named Rooms. Rooms was created by using the following transact-SQL statement.
You discover that some records in the Rooms table contain NULL values for the Owner field. You need to ensure that all future records have a value for the Owner field. What should you add?
Answer: D
Explanation:
The table definition allows Owner to be nullable because it was created as Owner nvarchar(100) without NOT NULL. Since the question asks what to add so that future rows must have a value, a check constraint such as CHECK (Owner IS NOT NULL) is the appropriate choice. Microsoft documents that check constraints validate future INSERT and UPDATE operations against the constraint condition.
The other options do not solve the requirement:
* A foreign key enforces referential integrity, not non-null entry by itself.
* A nonclustered index does not require values to be present.
* A unique constraint prevents duplicate values but still does not serve as the right mechanism here for enforcing presence across future writes. Microsoft's constraint documentation also notes that primary- key columns are implicitly NOT NULL, which helps distinguish nullability enforcement from other constraint types.
NEW QUESTION # 15
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 are creating a table that will store customer profiles.
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 # 16
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