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>> Valid DP-800 Exam Answers <<
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NEW QUESTION # 42
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: B
Explanation:
To protect sensitive Azure SQL database columns so that neither stored values nor query inputs reveal actual data--and to prevent inference of relationships/repetitions--Always Encrypted with Randomized Encryption is recommended. This approach ensures that data is encrypted on the client side, and the database engine never handles plaintext data or keys.
Key Recommendations:
Always Encrypted (Randomized): Use randomized encryption, which produces different encrypted values (ciphertext) for the same plaintext, preventing pattern analysis.
Azure Key Vault: Store column encryption keys in Azure Key Vault for secure, manageable key storage.
Client-side Encryption: Data is encrypted/decrypted within the application's memory using the SQL client driver, ensuring the database engine or high-privileged users cannot access the data.
Incorrect:
[Not D]
Deterministic Encryption: Allows matching queries (e.g., WHERE Column = 'Value'), but allows attackers to infer patterns and repetitions in data.
Transparent Data Encryption (TDE): Protects data at rest, but SQL users can still see plaintext data, violating the requirement.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-configure-always-encrypted-in-azure-sql- database/view
NEW QUESTION # 43
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: D
NEW QUESTION # 44
You have a SQL database in Microsoft Fabric that contains a table named WebSite. Logs. WebSite.Logs stores application telemetry data. Website.Logs contains a nvarehar(iMx) column named log that stores JSON documents You have a daily report that filters by the $.severity JSON property and returns Logld. LogDateTime, and log.
The report frequently causes full table scans.
You need to modify Website. Logs to support efficient filtering by $. severity and avoid key lookups for the columns returned by the report.
How should you complete the Transact-SQL code to avoid full table scans? 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 way to avoid full table scans here is to add a computed column that extracts the JSON scalar property with JSON_VALUE , and then create a nonclustered index on that computed column with the report's returned columns in the INCLUDE list. Microsoft's JSON indexing guidance specifically recommends creating a computed column that exposes the JSON property you filter on, using the same expression as in the query, and then indexing that computed column.
So the computed column must be:
AS JSON_VALUE([log], ' $.severity ' ) PERSISTED
This is correct because $.severity is a scalar JSON value, so JSON_VALUE is the proper function.
JSON_QUERY would be for extracting an object or array, not a scalar property. Microsoft also notes that persisted computed columns can improve access speed for JSON-derived values.
The index should then include:
INCLUDE (LogId, LogDateTime, [log])
That is the right covering strategy because the report filters by severity but returns LogId, LogDateTime, and log. Microsoft's guidance on included columns explains that nonkey included columns let a nonclustered index cover more queries and reduce extra lookups to the base table.
So the completed code is:
ALTER TABLE WebSite.Logs
ADD severity AS JSON_VALUE([log], ' $.severity ' ) PERSISTED;
GO
CREATE INDEX ix_severity
ON WebSite.Logs(severity)
INCLUDE (LogId, LogDateTime, [log]);
GO
NEW QUESTION # 45
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 # 46
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: C
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 # 47
......
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