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NEW QUESTION # 93
You create an SDK-style SQL database project in Microsoft Visual Studio Code named Database.sqlproj and add the project to a GitHub repository.
You need to configure a GitHub Actions workflow to support the planned changes for DB1.
How should you complete the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* pull_request
* build Database.sqlproj -c Release
The correct configuration is pull_request with dotnet build Database.sqlproj -c Release.
Fabrikam requires every pull request targeting production to be validated before merging, and all deployments must use the Release configuration.
Technical breakdown:
1. pull_request trigger: Executes the GitHub Actions workflow when a pull request targets the main branch.
This allows SQL project validation before changes are merged.
2. dotnet build: Compiles the SDK-style SQL database project, validates its database schema model, and produces a .dacpac deployment artifact.
3. -c Release: Explicitly selects the Release build configuration, satisfying Fabrikam ' s deployment configuration requirement.
4. Branch protection: Configure GitHub branch protection rules to make successful validation a required status check before merging. The workflow alone does not enforce this restriction.
5. Incorrect alternatives: push runs after changes are pushed, while merge is not a valid GitHub Actions event.
dotnet pack packages projects, and dotnet publish is not the appropriate SQL project validation command.
Microsoft identifies dotnet build as the supported compilation method for SDK-style SQL projects.
Completed GitHub Actions workflow
name: Validate SQL Project
on:
pull_request:
branches: [ " main " ]
jobs:
validate:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Step 1
run: dotnet build Database.sqlproj -c Release
DP-800 Official Study Guide Reference:
Main topic: Secure, optimize, and deploy database solutions.
Subtopic: Implement CI/CD by using SQL Database Projects.
Specific examination objectives:
* Create, build, and validate database models by using SQL Database Projects, including SDK-style models.
* Manage branching, pull requests, and conflict resolution.
* Design and implement controls for deployment pipelines.
NEW QUESTION # 94
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 # 95
Hotspot Question
You have an Azure SQL database named ProductsDB.
You deploy Data API builder (DAB) to Azure Container Apps.
You discover that the container app cannot connect to ProductsDB.
Your development team reports that the container app is unreachable from the internet for integration tests.
You need to update Azure SQL Database and Container Apps to meet the following requirements:
- Ensure that the Azure SQL logical server allows connections from
Azure services.
- Ensure that the Container Apps environment accepts inbound requests
from the public internet.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 96
Which Azure service is used for visualizing SQL + AI insights?
Answer: D
Explanation:
Microsoft Power BI creates dashboards and reports.
NEW QUESTION # 97
You have a SQL database in Microsoft Fabric named Sales BD that contains a table named dbo.Products. You need to modify SalesBD to meet the following requirements:
* Create a vector index on the appropriate column.
* Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The first correct selection is embedding because a vector index must be created on the vector column , not on a scalar distance column or a text column such as product_name. Microsoft's CREATE VECTOR INDEX documentation shows that the index is created directly on the vector-valued column, for example ON product_embeddings(embedding).
The second correct selection is VECTOR_SEARCH because the requirement is to use a supplied natural language query vector and search against the indexed embeddings. Microsoft documents that VECTOR_SEARCH is the Transact-SQL function for approximate nearest neighbor vector retrieval and that it applies to SQL database in Microsoft Fabric as well as other supported SQL platforms.
This also matches the shown code pattern:
* declare a vector variable such as @query_vector VECTOR(1536),
* create a vector index on dbo.Products(embedding),
* query with VECTOR_SEARCH(... SIMILAR_TO = @query_vector, METRIC = ' cosine ' , TOP_N =
10).
NEW QUESTION # 98
......
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