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NEW QUESTION # 50
Hotspot Question
You have an SDK-style SQL database project named MyDatabaseProject.sqlprojstored in a private GitHub repository. The repository contains the following GitHub Actions workflow.
The repository contains the AZURE_SQL_CONNECTION_STRING secrets.
The target is an Azure SQL database that allows access to Azure services and is configured to support mixed authentication.
The workflow runs successfully.
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 # 51
You have an Azure SQL database named ProductsDB.
You deploy Data API builder (DAB) to Azure Container Apps by using the [mcr.microsoft.com/azure- databases/data-api-builder:latest](https://mcr.microsoft.com/azure-databases/data-api-builder:latest) image.
The container app has the following configurations:
Secrets: mssql-connection-string, dab-config-base64
Environment variables:
MSSQL_CONNECTION_STRING=secretref:mssql-connection-string
DAB_CONFIG_BASE64=secretref:dab-config-base64
Ingress: External on port 5000
Users report that the /health endpoint returns a healthy response, but all requests that query an entity named Products fail and generate a connection error.
You confirm that the SQL login in the connection string is correct and the database exists.
You need to ensure that the container app can establish connections to the Azure SQL logical server without changing the container app deployment settings or the DAB configuration file.
What should you do on the Azure SQL logical server?
Answer: A
NEW QUESTION # 52
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 implement ProcedureDocuments to support the planned changes.
When users consume data through the Retrieval Augmented Generation (RAG) pattern, they experience data retrieval delays.
You need to improve the data retrieval performance and reduce the number of tokens per retrieval.
What should you implement?
Answer: C
Explanation:
Scenario: Fabrikam identifies the following AI-related requirements: Queries to the ProcedureDocuments table must use Reciprocal Rank Fusion (RRF).
To remedy data retrieval delays in a Retrieval Augmented Generation (RAG) pattern using Reciprocal Rank Fusion (RRF) on an Azure SQL Database table, you should use embeddings.
In a RAG architecture, retrieval delays often stem from inefficient or computationally heavy search processes. While RRF is excellent for merging results from multiple sources (like combining keyword and vector searches), the core of the speed problem typically lies in how the initial data is indexed and retrieved.
Role of Embeddings
Vector Search Acceleration: Embeddings convert text into high-dimensional vectors. Azure SQL Database can perform similarity searches on these vectors much faster than complex semantic text matching.
Hybrid Search Synergy: RRF is most effective when it fuses results from a keyword search (fast) and a vector search (powered by embeddings). Using embeddings ensures that the "semantic" side of the retrieval is streamlined.
Pre-computation: Since embeddings are generated once during ingestion, the retrieval phase only requires a distance calculation (e.g., Cosine Similarity), which is significantly faster than real- time natural language parsing during each query.
Reference:
https://pratikbarjatya.medium.com/unlocking-the-power-of-language-with-retrieval-augmented- generation-rag-14123cc275e6
NEW QUESTION # 53
Drag and Drop Question
You have an Azure SQL database that contains a table named dbo.Customers. dbo.Customers contains the following columns:
- CustomerId (int)(primary key)
- ProfileJson (nvarchar(max))
You have an application that returns phone numbers in a format of +000 000-000-0000. The phone numbers are stored in ProfileJson.
You need to write a query that returns:
- One row per customer
- A PhoneNUmerals column that contains only the digits
How should you complete the Transact-SQL query? 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 # 54
You have an Azure SQL database named DB1 that contains two tables named knowledgebase and query_cache. knowledge_base contains support articles and embeddings. query_cache contains chat questions, responses, and embeddings DB1 supports an Al-enabled chat agent.
You need to design a solution that meets the following requirements:
* Serializes the retrieved rows from knowledee_base
* Extracts the answer field from the response
* Extracts the embeddings to store in query_cache
You will call the external large language model (LLM) by using the sp_irwoke_external_re standpoint stored procedure.
Which Transact-SGL commands should you use for each requirement? To answer, drag the appropriate commands to the correct requirements. Each command may be used once, mote 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 mapping is:
FOR JSON PATH
JSON_VALUE
JSON_QUERY
To serialize the retrieved rows from knowledge_base, the correct command is FOR JSON PATH. Microsoft documents that FOR JSON formats query results as JSON, and PATH mode is the standard way to shape relational rows into JSON for downstream application or AI use.
To extract the answer field from the response, the correct command is JSON_VALUE because answer is a single scalar field. Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.
To extract the embeddings to store in query_cache, the correct command is JSON_QUERY because embeddings are returned as a JSON array, not a scalar. Microsoft states that JSON_QUERY extracts an object or array from JSON text, which is exactly the right behavior for an embeddings payload.
The unused options are not the best fit here:
OPENJSON is mainly for shredding JSON into rows and columns.
AI_GENERATE_CHUNKS is for chunking text, not extracting fields from a response payload.
VECTOR_DISTANCE computes similarity between vectors and is unrelated to JSON extraction.
FOR XML PATH produces XML, not JSON.
NEW QUESTION # 55
......
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