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NEW QUESTION # 67
Drag and Drop Question
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:
NEW QUESTION # 68
You have an Azure SQL database named SalesDB that contains tables named Sales.Orders and Sales.
OrderLines. Both tables contain sales data
You have a Retrieval Augmented Generation (RAG) service that queries SalesDB to retrieve order details and passes the results to a large language model (ILM) as JSON text. The following is a sample of the JSON.
You need to return one 1SON document per order that includes the order header fields and an array of related order lines. The LIM must receive a single JSON array of orders, where each order contains a lines property that is a JSON array of line Items.
Which transact-SQL commands should you use to produce the required JSON shape from the relational tables? To answer, drag the appropriate commands to the correct operations. Each command may be used once, more than once, or not at all. Vou 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:
* Serialize the order-level JSON : FOR JSON PATH
* Generate a nested lines array : JSON_QUERY
* Extract a single scalar value from the JSON text : JSON_VALUE
The correct mapping is based on how SQL Server and Azure SQL JSON functions are designed to shape relational data into JSON for AI and RAG scenarios.
To serialize the order-level JSON , use FOR JSON PATH . Microsoft documents that FOR JSON PATH gives you full control over the JSON output shape and formats the result as an array of JSON objects . It is the standard way to turn relational query results into the JSON structure needed by downstream consumers such as APIs and LLM-based RAG services. It also supports nested output through subqueries and aliases.
To generate a nested lines array , use JSON_QUERY . Microsoft explains that JSON_QUERY returns a JSON object or array from JSON text, and it is used when you want to preserve a JSON fragment instead of treating it as plain text. In this scenario, the nested lines property must be emitted as a proper JSON array inside each order document, so JSON_QUERY is the correct command to embed that array in the final JSON shape.
To extract a single scalar value from the JSON text , use JSON_VALUE . Microsoft explicitly states that JSON_VALUE extracts a scalar value from a JSON string, while JSON_QUERY is for objects or arrays. So whenever the requirement is to pull out one property such as an order number, currency code, or customer ID from JSON text, JSON_VALUE is the correct function.
The unused commands are not the best fit here:
* OPENJSON is primarily for parsing JSON into rows and columns, not for shaping relational tables into nested output.
* JSON_MODIFY is for updating JSON text, not generating the required output structure.
So the drag-and-drop answers are:
* Serialize the order-level JSON # FOR JSON PATH
* Generate a nested lines array # JSON_QUERY
* Extract a single scalar value from the JSON text # JSON_VALUE
NEW QUESTION # 69
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 need to meet the development requirements for the FeedbackJson column.
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 # 70
You are evaluating the DAB configuration.
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:
Explanation:
The answers follow from the role-based permissions and actions defined for each entity in the DAB configuration.
1. Procedures - Yes
The Procedures entity specifies " role " : " anonymous " and " actions " : [ " read " ]. This authorizes unauthenticated applications to retrieve procedure records through the REST API.
2. Transactions - No
The Transactions entity grants the authenticated role only read and create permissions. Applications can retrieve records and insert new transactions, but they cannot update existing records because the update action is absent.
3. UpdateProcedurePatient - Yes
The UpdateProcedurePatient entity grants the authenticated role the execute action. Its REST configuration enables HTTP POST requests through /procedurepatient.
Therefore, authenticated applications are authorized to invoke the stored procedure through the configured REST endpoint.
The graphql: false setting disables GraphQL exposure but does not disable REST functionality.
Important distinction: DAB treats create, read, update, delete, and execute as separate permissions. Granting one action does not automatically grant another.
For an actual deployment, the stored procedure should also be explicitly identified using source.type: " stored- procedure " and its database object name. The answer above reflects the permissions intended by the examination scenario.
DP-800 Official Study Guide Reference
Main topic: Secure, optimize, and deploy database solutions.
Relevant topics: Data API builder configuration, REST endpoints, role-based authorization, and database object permissions.
Official references:
* Microsoft Learn - DAB Authorization Overview
* Microsoft Learn - DAB Entity Configuration
NEW QUESTION # 71
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 # 72
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