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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, optimize, and deploy database solutions | 35-40% | - Optimize performance and reliability
|
| Topic 2: Implement AI capabilities in database solutions | 25-30% | - Build intelligent search and retrieval
|
| Topic 3: Design and develop database solutions | 35-40% | - Design database solutions
|
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NEW QUESTION # 46
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: A
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 # 47
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: C
Explanation:
The best explanation is an open explicit transaction . During the incident, session 72 was sleeping but still had open_transaction_count = 1 , and sys.dm_exec_input_buffer(72, NULL) showed only BEGIN TRANSACTION UPDATE Sales.Orders. That pattern indicates the session executed an update inside an explicit transaction and then remained idle without committing or rolling back , while still holding locks.
Other sessions showing blocking_session_id = 72 is the expected symptom of that situation. Microsoft explains that blocking occurs when one session holds a lock on a resource and another session requests a conflicting lock, and sleeping sessions can continue to block if they retain locks through an open transaction.
This also fits the observed behavior that the timeouts stopped only after session 72 was terminated . Killing the session would roll back the active transaction and release the locks, allowing waiting updates to continue.
That is much more consistent with an uncommitted transaction than with a deadlock, because deadlocks are normally detected and one session is chosen as the victim automatically rather than persisting until manual termination.
NEW QUESTION # 48
Drag and Drop Question
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 nvarchar (max) column named log that stores JSON documents.
You have a daily report that filters by the $.severity JSON property and returns LogId, 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:
NEW QUESTION # 49
You have an SDK-style SQL database project named MyDatabaseProject.sqlproj stored in a private GitHub repository. The repository contains the following GitHub Actions workflow:
YAML
name: Build and Deploy SQL Project
on:
push:
branches:
- main
workflow_dispatch:
jobs:
build-and-deploy:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@v4
- name: Build SQL project
run: dotnet build MyDatabaseProject.sqlproj
- name: Publish DACPAC
uses: azure/sql-action@v2
Answer:
Explanation:
Explanation:
NEW QUESTION # 50
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 enable similarity search to provide the analysts with the ability to retrieve the most relevant health summary reports. The solution must minimize latency. What should you include in the solution?
Answer: B
Explanation:
Scenario: There is a VehicleHealthSummary table.
To enable similarity search on your health summary data while minimizing latency, you should use the native VECTOR data type and a DiskANN vector index, which are now available in public preview for Azure SQL Database.
Solution Implementation
1. Define the Vector Column: Ensure your embeddings are stored using the native VECTOR(1536) type rather than NVARCHAR or VARBINARY. This format is optimized for high- dimensional data and mathematical operations.
ALTER TABLE HealthSummaries
ADD SummaryVector VECTOR(1536);
2. Create the Vector Index: Use the CREATE VECTOR INDEX statement. In Azure SQL, this uses the DiskANN algorithm, which is specifically designed to provide high-speed Approximate Nearest Neighbor (ANN) searches for large datasets.
CREATE VECTOR INDEX idx_health_summary_vector
ON HealthSummaries (SummaryVector)
WITH ( METRIC = 'COSINE', TYPE = 'DISKANN' );
3. Perform the Similarity Search: To leverage the index for low-latency retrieval, use the VECTOR_SEARCH function rather than VECTOR_DISTANCE. While VECTOR_DISTANCE calculates exact values (resulting in a full table scan), VECTOR_SEARCH utilizes the DiskANN index to find the most relevant reports quickly.
SELECT TOP(10) *
FROM HealthSummaries
ORDER BY VECTOR_DISTANCE('cosine', SummaryVector, @query_vector);
Reference:
https://learn.microsoft.com/en-us/samples/azure-samples/azure-sql-db-openai/azure-sql-db- openai/
NEW QUESTION # 51
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