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| Section | Objectives |
|---|---|
| Develop and manage database solutions | - Ensure security and compliance of data solutions - Optimize performance and scalability |
| Integrate AI capabilities with database systems | - Use Azure AI services with database workloads - Implement AI-assisted data processing |
| Monitor, troubleshoot, and maintain solutions | - Monitoring database health and performance - Troubleshooting data pipeline issues |
| Design and implement data solutions | - Design database solutions using Azure data services - Implement data storage and data processing solutions |
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NEW QUESTION # 77
You have a SQL database in Microsoft Fabric that contains a table named WebSite. Logs. WebSite.Logs stores application telemetry data. Website.Logs contains a nvarehar(iMx) column named log that stores JSON documents You have a daily report that filters by the $.severity JSON property and returns Logld. LogDateTime, and log.
The report frequently causes full table scans.
You need to modify Website. Logs to support efficient filtering by $. severity and avoid key lookups for the columns returned by the report.
How should you complete the Transact-SQL code to avoid full table scans? 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.
Answer:
Explanation:
Explanation:
The correct way to avoid full table scans here is to add a computed column that extracts the JSON scalar property with JSON_VALUE , and then create a nonclustered index on that computed column with the report's returned columns in the INCLUDE list. Microsoft's JSON indexing guidance specifically recommends creating a computed column that exposes the JSON property you filter on, using the same expression as in the query, and then indexing that computed column.
So the computed column must be:
AS JSON_VALUE([log], ' $.severity ' ) PERSISTED
This is correct because $.severity is a scalar JSON value, so JSON_VALUE is the proper function.
JSON_QUERY would be for extracting an object or array, not a scalar property. Microsoft also notes that persisted computed columns can improve access speed for JSON-derived values.
The index should then include:
INCLUDE (LogId, LogDateTime, [log])
That is the right covering strategy because the report filters by severity but returns LogId, LogDateTime, and log. Microsoft's guidance on included columns explains that nonkey included columns let a nonclustered index cover more queries and reduce extra lookups to the base table.
So the completed code is:
ALTER TABLE WebSite.Logs
ADD severity AS JSON_VALUE([log], ' $.severity ' ) PERSISTED;
GO
CREATE INDEX ix_severity
ON WebSite.Logs(severity)
INCLUDE (LogId, LogDateTime, [log]);
GO
NEW QUESTION # 78
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 to resolve the slow dashboard query issue. What should you recommend?
Answer: B
Explanation:
Scenario:
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.
To optimize this query, you should create a covering nonclustered index that handles both the filtering (WHERE) and the sorting (ORDER BY) requirements while including the remaining columns to avoid expensive lookups.
Recommended Index Strategy
Creating an index with FleetID as the first key column and LastUpdateUtc as the second key column will allow SQL Server to perform an Index Seek to find the specific fleet and then retrieve those rows in the pre-sorted order required by your ORDER BY clause.
T-SQL Implementation:
CREATE NONCLUSTERED INDEX IX_VehicleHealth_Fleet_Update
ON dbo.VehicleHealthSummary (FleetID, LastUpdateUtc DESC)
INCLUDE (EngineStatus, BatteryHealth);
Use code with caution.
Why this works
Eliminates Clustered Index Scan: The current plan scans the entire table because no index exists that starts with FleetID. This new index allows the engine to jump directly to the relevant rows.
Avoids a Sort Operator: By including LastUpdateUtc DESC in the index key, the data is already physically ordered. SQL Server can read the index and return the results immediately without needing a costly in-memory sort.
Fully Covers the Query: Using the INCLUDE clause for EngineStatus and BatteryHealth ensures all data required by the SELECT statement is present in the index. This prevents "Key Lookups," where the engine would otherwise have to go back to the original table for those specific values.
Reference:
https://www.mssqltips.com/sqlservertip/8192/sql-server-uses-non-clustered-index-rather-than- clustered-index
NEW QUESTION # 79
You have an Azure SQL database that supports the OLTP workload of an order-processing application.
During a 10-minute incident window, you run a dynamic management view query and discover the following:
- Session 72 is sleeping with open_transaction_count = 1.
- Multiple other sessions show blocking_session_id = 72 in
sys.dm_exec_requests.
- sys.dm_exec_input_buffer(72, NULL) returns only BEGIN TRANSACTION
UPDATE Sales.Orders.
Users report that updates to Sales.Orders intermittently time out during the incident window. The timeouts stop only after you manually terminate session 72.
What is a possible cause of the blocking?
Answer: A
Explanation:
This sounds like a classic orphaned transaction scenario.
The session was in a sleeping state with an open transaction, meaning the application sent the BEGIN TRANSACTION and the UPDATE statement, but then dropped the ball. Because SQL Server never received a COMMIT or ROLLBACK, it held onto the exclusive (X) locks on the Sales Order rows indefinitely.
Any other session trying to touch those same rows was forced to wait, leading to the blocking and eventual timeouts reported by your users. Manually killing the session forced a rollback, finally releasing the locks.
Reference:
https://learn.microsoft.com/en-ie/answers/questions/100075/sleeping-sessions-with-old-open- transactions-issue
NEW QUESTION # 80
You have an Azure SQL database named ToDo that contains a table named dbo.ToDo.
Your company plans to develop an Azure Functions app to run whenever the rows in dbo.ToDo change. The app will process INSERT, UPDATE, and DELETE events by using the Azure SQL trigger binding.
You need to configure ToDo to support the planned app.
What should you do?
Answer: B
NEW QUESTION # 81
You are developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat can call the hosted GitHub MCP Server tools by using OAuth.
The MCP server configuration must be scoped to the repository.
What should you do in Visual Studio Code?
Answer: A
NEW QUESTION # 82
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