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NEW QUESTION # 70
You need to create a table in the database to store the telemetry data. You have the following Transact-SQL code.
Answer:
Explanation:
Explanation:
The first statement is No . The requirement says telemetry data must be stored in a partitioned table to provide predictable performance for ingestion and retention operations. However, the shown CREATE TABLE statement does not define a partition function or partition scheme, and the table is created with a regular clustered primary key on TelemetryId. Microsoft's partitioning guidance states that creating a partitioned table requires a partition function , a partition scheme , and creating the table or index on that partition scheme using a partitioning column. None of that appears in the code, so the table is not partitioned.
The second statement is Yes . The code creates a JSON index named JI_VehicleTelemetry_Location on LocationJson for these specific JSON paths: $.location.latitude, $.location.longitude, and $.location.accuracy.
That matches the requirement that those JSON properties must be filterable by using an index seek .
Microsoft documents that JSON indexing is used to optimize filtering and sorting on JSON properties, and the index only helps for the properties included in the index definition.
The third statement is No . The JSON index is defined only for latitude, longitude, and accuracy. A query filtering on $.location.heading references a different path that is not included in the index definition, so that query would not use JI_VehicleTelemetry_Location for that predicate. JSON indexes are path-specific; they do not automatically cover unrelated properties in the same JSON document.
NEW QUESTION # 71
Your development team uses GitHub Copilot Chat in Microsoft SQL Server Management Studio (SSMS) to generate and run Transact-SQL queries against an Azure SQL database named DB1.
DB1 contains tables that store sensitive customer data.
You need to ensure that any Transact-SQL queries that run from GitHub Copilot Chat in SSMS are restricted by the same permissions as the developer's database login.
What prevents the GitHub Copilot Chat-run queries from accessing data beyond the developer's access?
Answer: A
Explanation:
GitHub Copilot Chat in SSMS acts as an extension of the user, meaning it does not have its own separate service account or elevated privileges.
It operates within the security context of your active connection. If your database login is restricted by Role-Based Access Control (RBAC), Row-Level Security (RLS), or specific DENY permissions on sensitive tables, Copilot cannot bypass those hurdles to fetch or manipulate data you couldn't otherwise access manually.
Reference:
https://learn.microsoft.com/en-us/ssms/github-copilot/chat
NEW QUESTION # 72
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements.
What should you include in the recommendation?
Answer: D
Explanation:
The best recommendation is to use an MCP server . In the official DP-800 study guide , Microsoft explicitly lists skills such as configuring Model Context Protocol (MCP) tool options in a GitHub Copilot session and connecting to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse . That makes MCP the exam-aligned mechanism for enabling AI-assisted tools to work with live database context rather than static snapshots.
This also matches the stated development requirement: the team will use Visual Studio Code and GitHub Copilot and needs to retrieve live metadata from the databases . Microsoft's documentation for GitHub Copilot with the MSSQL extension explains that Copilot works with an active database connection , provides schema-aware suggestions , supports chatting with a connected database, and adapts responses based on the current database context . Microsoft also documents MCP as the standard way for AI tools to connect to external systems and data sources through discoverable tools and endpoints.
The other options do not satisfy the "live metadata" requirement as well:
* A .dacpac is a point-in-time schema artifact, not live metadata.
* A Copilot instruction file provides guidance, not live database discovery.
* Including the database project in the repository helps source control and deployment, but it still does not provide live database metadata by itself.
NEW QUESTION # 73
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output Which should you use?
Answer: C
Explanation:
The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext , which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.
That makes randomized encryption the right choice here:
* It protects data at rest and in transit/query parameters under Always Encrypted's client-side encryption model.
* It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.
Why the other options are wrong:
* A. Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption .
* C. RLS controls row access, not value confidentiality.
* D. Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.
NEW QUESTION # 74
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:
Explanation:
The correct SARGable pattern for filtering a single calendar day is to use a half-open date range :
o.CreateDate > = @StartDate
AND o.CreateDate < @EndDate
with:
SET @EndDate = DATEADD(day, 1, @StartDate)
This is the correct design because it keeps the function off the column and applies it only to the parameter.
That allows SQL Server and Azure SQL to use an index on CreateDate efficiently, which is the key requirement for a SARGable predicate. Microsoft documents DATEADD as the standard function for adding one day to a date value, which makes it the right way to derive the exclusive upper boundary for the next day.
The incorrect choices are the ones that wrap CreateDate in CONVERT(...), because expressions like:
CONVERT(char(10), CreateDate, 121) = ...
make the predicate non-SARGable and typically prevent efficient seeks on an index over CreateDate.
So the completed procedure is:
CREATE PROCEDURE dbo.usp_SearchOrders
@StartDate date
AS
BEGIN
SET NOCOUNT ON;
DECLARE @EndDate date;
SET @EndDate = DATEADD(day, 1, @StartDate);
SELECT o.CreateDate,
o.OrderId,
o.ShipDate
FROM dbo.Orders AS o
WHERE o.CreateDate > = @StartDate
AND o.CreateDate < @EndDate;
END;
NEW QUESTION # 75
......
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