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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI capabilities in database solutions (25–30%) | 25-30% | - Integrate Azure AI services
|
| Topic 2: Design and develop database solutions (35–40%) | 35-40% | - Design and implement relational database schemas
|
| Topic 3: Secure, optimize, and deploy database solutions (35–40%) | 35-40% | - Optimize database performance
|
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NEW QUESTION # 64
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 # 65
You have a Microsoft SQL Server 2025 instance that contains a database named SalesDB SalesDB supports a Retrieval Augmented Generation (RAG) pattern for internal support tickets. The SQL Server instance runs without any outbound network connectivity.
You plan to generate embeddings inside the SQL Server instance and store them in a table for vector similarity queries.
You need to ensure that only a database user account named AlApplicationUser can run embedding generation by using the model.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Because the SQL Server 2025 instance has no outbound network connectivity , the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft's CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths . That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.
The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft's AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions . Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.
NEW QUESTION # 66
You have an Azure SQL database named SalesDB
You have a Data API builder (DAB) instance that exposes the following entities in SalesDB
* A table entity named Order mapped to a table named dbo. Orders
* A stored procedure entity named FinalizeOrder mapped to a stored procedure named dbo.usp_FinalizeOrder The DAB runtime configuration includes the following permissions.
Client requests include a Microsoft Entra access token. The client also sends HTTP header x-MS-APl-ROlE:
operations for both REST and GraphQL requests.
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:
* A REST GET request to the order entity that includes the access token and x-MS-API-ROLE:
operations will return data. # No
* When DAB runs the stored procedure, the database policy defined on the FinalizeOrder entity will be enforced. # Yes
* If the client omits the x-MS-API-ROLE header but still sends the same access token, the order entity read request will run in the authenticated role context. # Yes The first statement is No . In Data API builder, when a valid token is sent with X-MS-API-ROLE, the request runs in that requested role if that role is present in the token . Here, that means the effective role becomes operations , not authenticated. But the order entity grants read only to the authenticated role, not to operations, so the GET request would not be authorized to return data.
The second statement is Yes . DAB evaluates the request against the permissions and policies configured for the effective role on the requested entity. The FinalizeOrder entity grants execute to role operations and includes a database policy of TenantId = @claims.tenantid, so that policy is part of the enforced authorization
/filtering behavior when the stored procedure entity is executed.
The third statement is Yes . If the client sends a valid access token without X-MS-API-ROLE, DAB uses the built-in Authenticated system role by default. Since the order entity allows read for the authenticated role, that read request runs in the authenticated role context.
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NEW QUESTION # 67
You have an Azure SQL database that contains a table named Customer. Customer contains the following columns:
- NationalIDNumber is unique per customer.
- InvestigationNotes contains free-form text.
You have a stored procedure that performs point lookups by NationalIDNumber and returns InvestigationNotes in the query results without filtering on InvestigationNotes.
You need to encrypt both columns by using Always Encrypted and the highest security possible.
Which type of Always Encrypted encryption should you use for each column?
Answer: C
Explanation:
To achieve the highest security for an Azure SQL Database using Always Encrypted, you must use Randomized Encryption for the InvestigationNotes and Deterministic Encryption for the NationalIDNumber.
While "Randomized" is more secure, it does not support point lookups (equality searches).
Therefore, a hybrid approach is required to maintain functionality while maximizing protection.
Recommended Encryption Settings
* NationalIDNumber
Deterministic is required to allow the Stored Procedure to perform point lookups (WHERE NationalIDNumber = @id).
* InvestigationNotes
Use randomized as it provides the highest security. It prevents pattern matching and "leakage" for sensitive text.
Reference:
https://medium.com/@ih_hira/implementing-always-encrypted-with-azure-key-vault-and-entity- framework-core-in-azure-sql-part-1-3a0e5fd7ed9b
NEW QUESTION # 68
You need to generate embeddings to resolve the issues identified by the analysts. Which column should you use?
Answer: B
Explanation:
The correct column to use for generating embeddings is incidentDescrlption because embeddings are intended to represent the semantic meaning of rich textual content , not simple categorical, numeric, or location-only values. Microsoft's DP-800 study guide explicitly includes skills such as identifying which columns to include in embeddings , generating embeddings , and implementing semantic vector search for scenarios where users need to find similar records based on meaning rather than exact matches.
In this scenario, analysts report that it is difficult to find similar incidents based on details such as weather, traffic conditions, and location . Those are descriptive context elements that are typically captured in a free- text incident description field. An embedding generated from incidentDescrlption can encode the semantic relationships among these narrative details, making it suitable for similarity search , semantic search , and RAG retrieval . Microsoft documentation on vectors and embeddings explains that embeddings are generated from text data and then stored for vector search to find semantically related items.
The other options are weaker choices:
* vehicleLocation is too narrow and usually better handled with geospatial filtering , not embeddings.
* incidentType is likely categorical and too low in semantic richness.
* SeverityScore is numeric and not appropriate as the primary source for semantic embeddings.
Microsoft also notes that when multiple useful attributes exist, you can either embed each text column separately or concatenate relevant text fields into one textual representation before generating the embedding.
But among the options given, the best and most exam-aligned answer is the textual narrative column :
incidentDescrlption .
NEW QUESTION # 69
......
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