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>> Valid DP-800 Practice Questions <<
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NEW QUESTION # 25
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 # 26
You have an Azure SQL database named SalesDB that contains a table named dbo.Articles.
dbo.Articles contains two million articles with embeddings. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARCH.
Users report that semantic search results do NOT reflect the updates until the following day.
You need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB.
Which embedding maintenance method should you implement?
Answer: B
Explanation:
This setup is a solid way to bridge the gap between your SQL updates and your vector index. By using CDC (Change Data Capture), you avoid the heavy lifting of scanning the entire table for changes, which keeps your database CPU overhead low.
To make this work efficiently within the Azure Functions app, you'll typically want to:
Batch the updates: Instead of triggering a function for every single row change, process small batches of CDC records to reduce the number of calls to your embedding model (like OpenAI).
Use an Upsert logic: In your function, ensure the code handles both new inserts and updates by overwriting the existing embedding for a specific Article ID in the database.
Implement a "Cool-down" or Timer: If the "frequent updates" happen in bursts, you can use an Azure Service Bus or Storage Queue between the CDC process and the Function to smooth out the processing load.
Reference:
https://fivetran.com/docs/connectors/databases/troubleshooting/log-based-cdc-impact
NEW QUESTION # 27
Drag and Drop Question
You have an Azure SQL database that contains a table named dbo.Orders. dbo.Orders contains a column named CreateDate that stores order creation dates.
You need to create a stored procedure that filters Orders by CreateDate for a single calendar day. The solution must be SARGable.
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 # 28
Hotspot Question
You have an Azure SQL database that contains a table named dbo.SupportTickets.
dbo.SupportTickets contains a JSON column named Payload and a datetime column CreatedAt.
You need to generate a report for the last seven days that meets the following requirements:
- Returns exactly one row per customer per day
- For each customer and day, returns the earliest ticket
- Includes the customer ID stored in Payload
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 29
You have an Azure SQL database named sqldb-ai-prod that stores customer support tickets for a multitenant software as a service (SaaS) application. sqldb-ai-prod contains a table named Tickets. Tickets contains columns named TenantId, TicketId, CustomerEmail, CustomerPhone, and Notes.
You plan to harden data access, since a new support team will use ad hoc reporting tools that connect directly to sqldb-ai-prod.
You need to configure security to meet the following requirements:
* Support agents must see only the rows of their own TenantId column.
* Support agents must see only the domain name portion of the CustomerEmail column.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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:
Verified Answer : =
* Support agents must see only the rows of their own TenantId # Create a row-level security (RLS) predicate that uses the user context.
* Support agents must see only the domain name portion of CustomerEmail # Configure dynamic data masking on selected columns.
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = For the TenantId requirement, use Row-Level Security (RLS) . RLS is designed to restrict which rows a database principal can access. A security predicate can compare the row ' s TenantId with tenant information derived from the current user ' s session or security context, so support agents querying the same Tickets table see only rows belonging to their own tenant. This is the correct multitenant isolation mechanism because it is enforced by the database rather than relying on individual reporting tools or application-side filtering.
For CustomerEmail , use Dynamic Data Masking (DDM) . DDM controls how sensitive column values are displayed to users who do not have UNMASK permission. A partial masking rule can conceal the local/user- specific portion of an email address while exposing only the required portion, such as its domain, according to the masking configuration.
The remaining choices do not satisfy these specific requirements. Transparent Data Encryption (TDE) protects database files at rest but does not filter rows or selectively obscure query results. Always Encrypted protects sensitive values cryptographically and is appropriate when the database engine itself should not see plaintext, but it is not the right mechanism when support agents must deliberately see a controlled portion of an email address.
Final drag-and-drop selections:
* Own TenantId rows # Create a row-level security (RLS) predicate that uses the user context
* Only domain portion of CustomerEmail # Configure dynamic data masking on selected columns
NEW QUESTION # 30
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