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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, optimize, and deploy database solutions | 35-40% | - Secure database solutions
|
| Topic 2: Implement AI capabilities in database solutions | 25-30% | - Use AI-assisted development tools
|
| Topic 3: Design and develop database solutions | 35-40% | - Develop database solutions
|
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NEW QUESTION # 62
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.
Hotspot Question
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:
NEW QUESTION # 63
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 # 64
You have an Azure SQL database named SalesDB that supports an ecommerce application. SalesDB contains a table named dbo.Orders that has a clustered index on a column named orderid.
dbo.Orders receives continuous OLTP inserts and updates during business hours.
Your analytics team runs hourly aggregate queries that scan dbo.Orders to calculate revenue trends for recent dates.
You need to improve the performance of the hourly analytics queries without significantly affecting OLTP throughput. The solution must meet the following requirements:
Support near-real-time (NRT) analytics on the dbo.Orders table.
Reduce read time when retrieving analytical data from dbo.Orders.
Support indexing only rows that match a predicate, such as Active = 1.
Which type of index should you use for each requirement? To answer, drag the appropriate index types to the correct requirements. Each index type 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:
NEW QUESTION # 65
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 generate embeddings to resolve the issues identified by the analysts. Which column should you use?
Answer: C
Explanation:
Scenario: 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.
The column you should use to generate embeddings is IncidentDescription.
Why this is the correct choice:
Semantic Meaning: Embeddings are designed to capture the semantic meaning of rich, unstructured text. The IncidentDescription column likely contains the detailed natural language notes about weather, traffic, and location that the analysts are struggling to search effectively using standard methods.
Vector Search: By converting the text in IncidentDescription into mathematical vectors (embeddings), you can perform a semantic vector search. This allows the database to find
"similar" incidents based on the context of the description rather than just matching exact keywords or categories.
Incorrect:
[Not A]
VehicleLocation: Typically contains specific coordinates or addresses which are better handled by spatial data types or exact matching.
[Not C]
IncidentType: Is likely a categorical label (e.g., "Fender Bender", "Multi-car pileup") and too simple for meaningful embedding generation.
[Not D]
SeverityScore: Is a numeric value that represents a magnitude, not a semantic concept.
Reference:
https://learn.microsoft.com/en-us/sql/t-sql/spatial-geography/spatial-types-geography
NEW QUESTION # 66
Drag and Drop Question
You have an Azure SQL database that contains a table named Sales.Orders. Sales.Orders contains the following columns.
Reporting queries frequently repeat logic to calculate the number of days since an order was placed.
You need to create a scalar user-defined function (UDF) that returns the number of days between an input value of @OrderDate and the current date and time.
How should you complete the Transact-SQL code? 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 # 67
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