Correct Answer:

Explanation:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.

Correct Answer: C  🗳️

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Correct Answer:

Explanation:
* PhoneRaw extraction # JSON_VALUE(c.ProfileJson, ' $.contact.phone ' )
* PhoneNumerals # REGEXP_REPLACE(p.PhoneRaw, ' [^0-9] ' , ' ' )
* WHERE condition # p.PhoneRaw
The first correct selection is JSON_VALUE(c.ProfileJson, ' $.contact.phone ' ) because the phone number is stored as a scalar JSON property . Microsoft documents that JSON_VALUE extracts a scalar value from JSON text, whereas JSON_QUERY is intended for JSON objects or arrays.
The second selection is REGEXP_REPLACE(p.PhoneRaw, ' [^0-9] ' , ' ' ) . The regular expression [^0-9] matches every character that is not a digit . Replacing all such characters with an empty string converts a formatted value such as:
+000 000-000-0000
into:
0000000000000
Microsoft documents that REGEXP_REPLACE returns the source string after occurrences matching the regular expression are replaced, and it applies to Azure SQL Database.
The final blank is p.PhoneRaw , producing:
WHERE p.PhoneRaw IS NOT NULL;
This excludes customers that do not have a phone value while preserving one output row for each customer that does.
The completed query is:
WITH PhoneCTE AS
(
SELECT DISTINCT
c.CustomerId,
JSON_VALUE(c.ProfileJson, ' $.contact.phone ' ) AS PhoneRaw
FROM dbo.Customers AS c
)
SELECT
p.CustomerId,
REGEXP_REPLACE(p.PhoneRaw, ' [^0-9] ' , ' ' ) AS PhoneNumerals
FROM PhoneCTE AS p
WHERE p.PhoneRaw IS NOT NULL;
Final drag-and-drop selections:
* First target: JSON_VALUE(c.ProfileJson, ' $.contact.phone ' )
* Second target: REGEXP_REPLACE(p.PhoneRaw, ' [^0-9] ' , ' ' )
* Third target: p.PhoneRaw

Correct Answer:

Explanation:
Verified Answer : =
* CPU aggregation expression # rs.avg_cpu_time
* Runtime interval source # sys.query_store_runtime_stats_interval
* Last two hours filter # DATEADD(HOUR, -2, GETUTCDATE())
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first correct selection is rs.avg_cpu_time . Query Store stores CPU statistics per aggregation interval, and Microsoft documents avg_cpu_time as the average CPU time per execution, in microseconds . To calculate total CPU consumption across all executions, multiply count_executions by avg_cpu_time , then divide by
1000.0 to convert microseconds to milliseconds:
SUM(count_executions * rs.avg_cpu_time / 1000.0)
This is the exact pattern Microsoft uses in its documented query for identifying the top 15 CPU-consuming queries by query hash .
The second selection is sys.query_store_runtime_stats_interval because sys.query_store_runtime_stats.
runtime_stats_interval_id is a foreign key to this view. The interval view provides start_time and end_time , allowing Query Store data to be limited to the required time window.
The third selection is DATEADD(HOUR, -2, GETUTCDATE()) . Microsoft's own Azure SQL performance- monitoring example for the last two hours uses exactly:
rsi.start_time > = DATEADD(HOUR, -2, GETUTCDATE())
and then ranks by total CPU descending to return the top 15 query hashes.
Final drag-and-drop selections:
* First target: rs.avg_cpu_time
* Second target: sys.query_store_runtime_stats_interval
* Third target: DATEADD(HOUR, -2, GETUTCDATE())