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| Section | Weight | Objectives |
|---|---|---|
| Design and develop database solutions | 35-40% | - Design database solutions
|
| Implement AI capabilities in database solutions | 25-30% | - Build intelligent search and retrieval
|
| Secure, optimize, and deploy database solutions | 35-40% | - Optimize performance and reliability
|
>> Question Microsoft DP-800 Explanations <<
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NEW QUESTION # 28
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 # 29
You have a database that contains production data. The schema is stored in a Git repository as an SDK-style SQL database project and contains the following reference data.
Name
Data type
RefID
int, identity
Code
nvarchar(10)
CreatedDate
datetime2
Description
nvarchar(255)
A deployment pipeline can be rerun automatically when a transient failure occurs.
You need to deploy the reference data as part of the same CI/CD process. Rerunning the pipeline must produce the same outcome and must NOT create duplicate rows.
What should you do?
Answer: A
NEW QUESTION # 30
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 need to use the UsefulPrompts table as defined in the AI requirements. Which stored procedure should you use?
Answer: D
Explanation:
Scenario:
The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
The system stored procedure sp_invoke_external_rest_endpoint is used to connect an Azure SQL Database to an Azure OpenAI endpoint.This procedure allows you to call HTTPS REST endpoints directly from your database, enabling the integration of generative AI or embedding models into your SQL workflows without an intermediate application layer.
Reference:
https://blog.fabric.microsoft.com/en-gb/blog/ai-ready-apps-from-rag-to-chat-interacting-with-sql- database-in-microsoft-fabric-using-graphql-and-mcp
NEW QUESTION # 31
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: D
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 # 32
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: B
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 # 33
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