Microsoft Administering Relational Databases on Microsoft Azure DP-300 Exam Dumps: Updated Questions & Answers (July 2026)

Create an Azure Monitor metric alert rule on database db1 using the metric:

CPU percentage

Configure the condition as:

Aggregation: Average

Operator: Greater than

Threshold: 50

Aggregation granularity / Period: 1 minute

Frequency of evaluation: 1 minute

Evaluation period / Lookback window: 5 minutes

Action group email: admin@contoso.com

Azure SQL Database exposes CPU percentage as a platform metric, and Azure Monitor metric alerts can send notifications through action groups such as email. Microsoft describes SQL Database alerts as metric-based alerts that can send email when metrics such as CPU usage reach a defined threshold.

Azure Portal Method — Recommended for Simulation

Step 1: Open the db1 Azure SQL database

Sign in to the Azure portal.

Search for SQL databases.

Open the database named:

db1

Do not open the SQL logical server unless the alert needs to apply to all databases. This task is specifically for db1, so the alert scope must be the db1 database resource.

Step 2: Create a new alert rule

From the db1 database page:

In the left menu, select Alerts.

Select Create.

Select Alert rule.

Microsoft’s Azure Monitor workflow allows you to create an alert rule directly from the target resource. When you create it from a resource, the resource is automatically set as the alert scope.

Step 3: Confirm the alert scope

On the alert rule page, confirm the scope is the Azure SQL database:

db1

The resource type should be similar to:

SQL database

Microsoft.Sql/servers/databases

If the scope is the SQL server instead of the database, remove it and select the db1 database resource.

Step 4: Add the alert condition

Under Condition, select Add condition.

Choose the metric:

CPU percentage

Sometimes it appears as:

CPU percent

or metric name:

cpu_percent

For Azure SQL Database, CPU percentage represents CPU consumption toward the database workload limit, expressed as a percentage.

Step 5: Configure the signal logic

Configure the alert logic exactly like this:

Setting

Value

Threshold type

Static

Aggregation type

Average

Operator

Greater than

Threshold value

50

Unit

Percent

Aggregation granularity / Period

1 minute

Frequency of evaluation

1 minute

Evaluation period / Lookback window

5 minutes

This means Azure Monitor evaluates the CPU metric every minute, using one-minute metric samples, and fires the alert only when the average CPU percentage is greater than 50% across the five-minute evaluation window.

Be precise here. The task says:

average CPU percentage utilization is greater than 50 percent for five minutes sampled at one-minute intervals

So the correct choices are:

Average

Greater than 50

Every 1 minute

Over the last 5 minutes

Azure Monitor metric alert rules combine the monitored resource, the metric condition, and action groups that run when the condition is met.

Step 6: Create the email action group

Under Actions, select:

Create action group

Configure the basics:

Setting

Value

Subscription

Use the current subscription

Resource group

Use the lab resource group

Action group name

AG-db1-CPU-Email

Display name

db1CPU

Then go to Notifications.

Add a notification:

Setting

Value

Notification type

Email/SMS message/Push/Voice

Name

EmailAdmin

Email

admin@contoso.com

Select OK, then Review + create, then Create.

Action groups define the notification or automation action that runs when an alert fires. Microsoft documents email as a supported action group notification type.

Step 7: Configure alert rule details

Under Details, configure:

Setting

Value

Severity

2 or 3

Alert rule name

db1 CPU greater than 50 percent

Description

Alert when average CPU percentage for db1 is greater than 50 percent for 5 minutes.

Enable upon creation

Yes

Severity is usually not specified by the task, so any reasonable severity is acceptable. In an exam lab, I would use Severity 2 for CPU performance impact.

Step 8: Review and create

Select Review + create.

Confirm the condition says something equivalent to:

Whenever the average CPU percentage is greater than 50

Confirm the evaluation settings are:

Check every 1 minute

Lookback period 5 minutes

Confirm the action group sends email to:

admin@contoso.com

Select Create.

Verification

After creation:

Open db1.

Go to Alerts.

Select Alert rules.

Confirm the alert rule exists and is enabled.

Open the rule and verify:

Scope: db1

Signal: CPU percentage

Aggregation: Average

Operator: Greater than

Threshold: 50

Evaluation frequency: 1 minute

Window size: 5 minutes

Action group: admin@contoso.com

Azure CLI Method

Use this only if the simulation gives you Cloud Shell.

First get the database resource ID:

az sql db show \

--resource-group < resource-group-name > \

--server < sql-server-name > \

--name db1 \

--query id \

--output tsv

Create the action group:

az monitor action-group create \

--resource-group < resource-group-name > \

--name AG-db1-CPU-Email \

--short-name db1CPU \

--action email EmailAdmin admin@contoso.com

Create the metric alert:

az monitor metrics alert create \

--name " db1 CPU greater than 50 percent " \

--resource-group < resource-group-name > \

--scopes < db1-resource-id > \

--condition " avg cpu_percent > 50 " \

--window-size 5m \

--evaluation-frequency 1m \

--action AG-db1-CPU-Email \

--description " Alert when average CPU percentage for db1 is greater than 50 percent for 5 minutes. "

The metric name commonly used for Azure SQL Database CPU percentage in CLI/ARM contexts is:

cpu_percent

SSMS Clarification

SSMS is not the correct tool for this task.

Do not configure Database Mail. Azure SQL Database does not use SQL Server Agent/Database Mail in the same way as SQL Server on a VM or SQL Managed Instance. This requirement is an Azure Monitor metric alert requirement.

Correct tool:

Azure portal > db1 > Alerts > Create alert rule

Wrong tool:

SSMS Database Mail

Final Exam-Lab Configuration

Use this exact configuration:

Resource: db1

Alert type: Metric alert

Metric: CPU percentage

Aggregation: Average

Operator: Greater than

Threshold: 50

Aggregation granularity: 1 minute

Evaluation frequency: 1 minute

Evaluation period/window: 5 minutes

Action group notification: Email

Email recipient: admin@contoso.com

That completes the task.

Enable Automatic tuning at the server level and turn Create Index to On.

Microsoft states that Azure SQL Database automatic tuning can create indexes automatically, verify performance improvement, and roll back changes if performance regresses. Server-level automatic tuning settings are applied to all databases on the server by default, unless a database has its own override.

Azure Portal Method — Recommended for Simulation

Step 1: Open the SQL logical server

Sign in to the Azure portal.

Search for SQL servers.

Open:

sql60152867

Do not open only db1. The task says all databases on sql60152867, so configure this at the server level.

Step 2: Open Automatic tuning

In the SQL server left menu:

Go to Intelligent Performance.

Select Automatic tuning.

Microsoft’s documented path is to open the Azure SQL server in the portal and select Automatic tuning from the menu.

Step 3: Enable automatic index creation

On the Automatic tuning page, configure:

Setting

Value

Create index

On

Drop index

Leave unchanged unless required

Force last good plan

Leave unchanged unless required

The only required setting is:

Create index = On

Do not confuse this with Drop index. The task only asks to ensure indexes are created automatically.

Step 4: Apply the setting

Select Apply or Save.

Wait for the portal confirmation.

The setting is now configured at the SQL logical server level.

Important Database Inheritance Check

This is the part people miss.

Server-level automatic tuning applies to databases that inherit from the server. Microsoft states that individual databases can override server-level automatic tuning settings. Therefore, if the simulation shows any database with custom automatic tuning settings, set that database to Inherit from server or manually set Create index = On for that database.

For each database, the correct inherited state should be:

Automatic tuning = Inherit from server

Create index = Inherited / On

If a database is set to:

Custom

Create index = Off

then the server-level setting might not affect that database. Fix it.

Database-Level T-SQL Method

Use this only if the portal is unavailable or you need to correct a specific database override.

Connect to each database and run:

ALTER DATABASE CURRENT

SET AUTOMATIC_TUNING (

CREATE_INDEX = ON

);

Microsoft documents this exact syntax for enabling the CREATE_INDEX automatic tuning option by T-SQL on an individual Azure SQL Database.

To make an individual database inherit server settings, run:

ALTER DATABASE CURRENT

SET AUTOMATIC_TUNING = INHERIT;

But this must be executed inside each database, not from master as a single server-wide T-SQL command.

Verification with T-SQL

Connect to a database and run:

SELECT

name,

desired_state_desc,

actual_state_desc

FROM sys.database_automatic_tuning_options

WHERE name = ' CREATE_INDEX ' ;

Expected result:

name CREATE_INDEX

desired_state_desc ON or DEFAULT

actual_state_desc ON

If desired_state_desc is DEFAULT, that usually means the database is inheriting the server-level setting. The key result is:

actual_state_desc = ON

SSMS Clarification

SSMS can be used to verify or configure the setting per database using T-SQL, but it is not the best tool for the requirement as written.

Because the task says:

all the databases on sql60152867

the correct primary method is:

Azure portal > SQL server > Automatic tuning > Create index = On

Final Exam-Lab Action

Configure this:

SQL server: sql60152867

Automatic tuning

Create index: On

Apply

Then verify databases are inheriting the server setting or have CREATE_INDEX = ON.

That completes the task.

Configure Dynamic Data Masking on the email column in:

SalesLT.Customer

The column is normally:

EmailAddress

Use the built-in masking function:

email()

Microsoft documents that Dynamic Data Masking hides sensitive data in query results for nonprivileged users without changing the stored data. The built-in email() masking function exposes the first letter and returns the masked format aXXX@XXXX.com, which exactly matches the requirement.

Method 1 — SSMS / T-SQL Method

This is the fastest and most reliable method.

Step 1: Connect to db1

Open SQL Server Management Studio.

Connect to the Azure SQL logical server that hosts db1.

Open a query window against database:

db1

Step 2: Apply the email mask

Run:

ALTER TABLE [SalesLT].[Customer]

ALTER COLUMN [EmailAddress]

ADD MASKED WITH (FUNCTION = ' email() ' );

This adds a Dynamic Data Masking rule to the EmailAddress column. The actual email address remains stored in the table, but users without permission to view unmasked data will see the masked value. Microsoft’s documented syntax for adding an email mask is ALTER COLUMN Email ADD MASKED WITH (FUNCTION = ' email() ' ).

Step 3: Verify that the column is masked

Run:

SELECT

OBJECT_SCHEMA_NAME(mc.object_id) AS schema_name,

OBJECT_NAME(mc.object_id) AS table_name,

c.name AS column_name,

mc.masking_function

FROM sys.masked_columns AS mc

JOIN sys.columns AS c

ON mc.object_id = c.object_id

AND mc.column_id = c.column_id

WHERE OBJECT_SCHEMA_NAME(mc.object_id) = ' SalesLT '

AND OBJECT_NAME(mc.object_id) = ' Customer '

AND c.name = ' EmailAddress ' ;

Expected result:

schema_name SalesLT

table_name Customer

column_name EmailAddress

masking_function email()

Step 4: Test as a non-administrative user

If you have a test user, run:

EXECUTE AS USER = ' TestUser ' ;

SELECT TOP (10)

EmailAddress

FROM SalesLT.Customer;

REVERT;

Expected output should look like:

aXXX@XXXX.com

bXXX@XXXX.com

cXXX@XXXX.com

A user with administrative privileges, db_owner, or UNMASK permission can still see the original email value. Microsoft states that users with administrative rights such as server admin, Microsoft Entra admin, and db_owner can view the original unmasked data.

Method 2 — Azure Portal Method

Use this if the simulation expects portal configuration.

Step 1: Open db1

Sign in to the Azure portal.

Search for SQL databases.

Open database:

db1

Step 2: Open Dynamic Data Masking

From the db1 page:

Security > Dynamic Data Masking

Microsoft states that for Azure SQL Database, Dynamic Data Masking can be configured in the Azure portal from the SQL database configuration pane under Security > Dynamic Data Masking.

Step 3: Add a masking rule

Add a mask for the email column:

Setting

Value

Schema

SalesLT

Table

Customer

Column

EmailAddress

Masking field format

Email

Masking function

email()

Then select:

Save

The portal may show the mask type simply as:

Email

That is the correct option because it maps to the email() masking function.

Important Permission Check

Dynamic Data Masking only affects users who do not have permission to view unmasked data.

If a non-administrative user was previously granted UNMASK, remove it:

REVOKE UNMASK TO [UserName];

Or, if a role was granted UNMASK, revoke it from the role:

REVOKE UNMASK TO [RoleName];

Do not grant UNMASK to normal users. UNMASK allows users to bypass masking and see the original values. Microsoft documents that UNMASK permission controls whether users can view masked or original data.

Final Exam-Lab Action

Run this against db1:

ALTER TABLE [SalesLT].[Customer]

ALTER COLUMN [EmailAddress]

ADD MASKED WITH (FUNCTION = ' email() ' );

Then verify:

SELECT

OBJECT_SCHEMA_NAME(object_id) AS schema_name,

OBJECT_NAME(object_id) AS table_name,

name AS column_name,

masking_function

FROM sys.masked_columns

WHERE OBJECT_SCHEMA_NAME(object_id) = ' SalesLT '

AND OBJECT_NAME(object_id) = ' Customer '

AND name = ' EmailAddress ' ;

The task is complete when non-administrative users querying SalesLT.Customer.EmailAddress see masked email values such as:

aXXX@XXXX.com

Requirement: Configure active geo-replication for Azure SQL Database db1. The geo-replica must be created in East US or East US 2 on the logical Azure SQL server:

sql60152867-dr.database.windows.net

The important point: in Azure SQL, the logical server name used in portal/T-SQL is usually:

sql60152867-dr

not the full FQDN.

Microsoft states that active geo-replication is configured per database, and a geo-secondary is created for an existing Azure SQL Database. After creation and seeding, changes from the primary are replicated asynchronously to the secondary.

Method 1 — Azure Portal Method

This is the safest method for the simulation if the portal is available.

Step 1: Open the primary database

Sign in to the Azure portal.

Search for SQL databases.

Select the database named db1.

Confirm you are looking at the primary database, not an existing secondary.

Step 2: Open the Replicas blade

In the left menu of the db1 database page, scroll to Data management.

Select Replicas.

Select Create replica.

Microsoft’s portal workflow is: open the database, go to Data management > Replicas, and choose Create replica.

Step 3: Configure the geo-secondary replica

On the Create SQL Database replica page, configure the target like this:

Setting

Value

Database

db1

Replica type

Geo replica / Active geo-replication

Target server

sql60152867-dr

Region

East US or East US 2

Database name

db1

Compute + storage

Keep same or compatible with primary

Elastic pool

Only choose this if the lab specifically requires an elastic pool

Do not create a failover group unless the task asks for one. This task says active geo replication, so configure a database-level geo-replica, not a failover group. Microsoft explicitly separates active geo-replication from failover groups and notes that active geo-replication is configured per database.

Step 4: Review and create

Select Review + create.

Confirm the target server is:

sql60152867-dr

Confirm the region is either:

East US

or

East US 2

Select Create.

Azure will create the secondary database and begin the seeding process. Microsoft notes that the secondary database has the same name as the primary by default and begins replication after it is created and seeded.

Step 5: Verify replication

After deployment completes:

Go back to the primary database db1.

Open Replicas again.

Under Geo replicas, confirm that a replica exists on:

sql60152867-dr.database.windows.net

Confirm the replica status is healthy, online, or synchronizing.

You can also open the target SQL server sql60152867-dr and verify that a database named db1 now exists there.

Method 2 — SSMS / T-SQL Method

Use this method if the portal is awkward or the exam simulation expects T-SQL.

Step 1: Allow SSMS connectivity

Before connecting with SSMS:

In Azure portal, open the primary SQL server hosting db1.

Go to Networking or Firewalls and virtual networks.

Add your client IP address.

Repeat this on the secondary server:

sql60152867-dr.database.windows.net

This matters because SSMS must be able to connect to the Azure SQL logical server.

Step 2: Connect to the primary server in SSMS

Open SQL Server Management Studio.

Connect to the primary Azure SQL logical server that hosts db1.

Use SQL admin credentials or Microsoft Entra admin credentials.

In Connection Properties, connect to the database:

master

This is important. For Azure SQL Database geo-replication setup through T-SQL, run the command from the master database on the primary server.

Step 3: Run the active geo-replication command

Run this query:

ALTER DATABASE [db1]

ADD SECONDARY ON SERVER [sql60152867-dr]

WITH (ALLOW_CONNECTIONS = ALL);

Microsoft documents that ALTER DATABASE ... ADD SECONDARY ON SERVER creates a secondary database for an existing Azure SQL Database and starts data replication. The official example also uses WITH (ALLOW_CONNECTIONS = ALL) to create a readable geo-secondary.

Step 4: Verify the replication link

Still connected to the primary server, run:

SELECT *

FROM sys.geo_replication_links;

Or use:

SELECT *

FROM sys.dm_geo_replication_link_status;

Microsoft lists sys.geo_replication_links and sys.dm_geo_replication_link_status as views used to return information about existing replication links and replication status.

Optional PowerShell Method

Only use this if the lab gives you Cloud Shell or PowerShell access.

New-AzSqlDatabaseSecondary `

-ResourceGroupName " < PrimaryResourceGroupName > " `

-ServerName " < PrimaryServerName > " `

-DatabaseName " db1 " `

-PartnerResourceGroupName " < SecondaryResourceGroupName > " `

-PartnerServerName " sql60152867-dr " `

-PartnerDatabaseName " db1 "

Microsoft identifies New-AzSqlDatabaseSecondary as the PowerShell cmdlet that creates a secondary database for an existing Azure SQL Database and starts replication.

Final Answer / What You Must Achieve

The task is complete when:

Database db1 still exists on the primary server.

A secondary database named db1 exists on:

sql60152867-dr.database.windows.net

The target server is in East US or East US 2.

The replica appears under db1 > Replicas > Geo replicas.

Replication status is healthy, online, seeding, or synchronizing.

You did not configure a failover group unless separately requested.

To prevent db1 from reverting to the last known good query plan, you need to disable the automatic plan correction feature for the database. This feature is enabled by default and allows the Query Store to detect and fix plan performance regressions by forcing the last good plan1. However, if you want to test the performance of different plans without interference from the Query Store, you can turn off this feature by using the ALTER DATABASE SCOPED CONFIGURATION statement2.

Here are the steps to disable the automatic plan correction feature for db1:

Connect to db1 using SQL Server Management Studio, Azure Data Studio, or any other tool that supports Transact-SQL statements.

Open a new query window and run the following command: ALTER DATABASE SCOPED CONFIGURATION SET AUTOMATIC_TUNING (FORCE_LAST_GOOD_PLAN = OFF); GO

This command will disable the automatic plan correction feature for db1 and allow the Query Optimizer to choose the best plan based on the current statistics and parameters3.

To verify that the automatic plan correction feature is disabled for db1, you can query the sys.database_scoped_configurations catalog view. The value of the force_last_good_plan column should be 0 for db1.

These are the steps to disable the automatic plan correction feature for db1.

To ensure that db1 is compatible with all the features and syntax of SQL Server 2012, you need to set the compatibility level of the database to 110, which is the compatibility level for SQL Server 20121. The compatibility level affects the behavior of certain Transact-SQL statements and features, and determines how the database engine interprets the SQL code2.

You can set the compatibility level of db1 by using the Azure portal or Transact-SQL statements. Here are the steps for both methods:

Using the Azure portal:

Go to the Azure portal and select your Azure SQL Database server that hosts db1.

Select the database db1 and click on Query Performance Insight in the left menu.

Click on Configure Query Store and select 110 from the Compatibility level dropdown list.

Click on Save to apply the change.

Using Transact-SQL statements:

Connect to db1 using SQL Server Management Studio, Azure Data Studio, or any other tool that supports Transact-SQL statements.

Open a new query window and run the following command: ALTER DATABASE db1 SET COMPATIBILITY_LEVEL = 110; GO

This command will set the compatibility level of db1 to 110, which is equivalent to SQL Server 2012.

These are the steps to set the compatibility level of db1 to 110. 

Basic Concept: This question tests plan and implement data platform resources in the DP-300 exam context. The correct choice is determined by the exact service boundary and operational requirement stated in the scenario.

Why D and E is Correct: Select the Classification page. is correct because it is the feature whose normal purpose matches the stated requirement. Select the Classification page. is an Azure data-platform feature, but it must match the workload model, service tier, migration path, or deployment constraint described in the scenario. The scenario wording points to that specific behavior: You need to identify the process for creating a custom sensitivity label and assigning it to the CustomerlD column. Select Configure is correct because it is the feature whose normal purpose matches the stated requirement. Select Configure is an Azure data-platform feature, but it must match the workload model, service tier, migration path, or deployment constraint described in the scenario. The scenario wording points to that specific behavior: You need to identify the process for creating a custom sensitivity label and assigning it to the CustomerlD column.

Why A is Wrong: Select Export is an Azure data-platform feature, but it must match the workload model, service tier, migration path, or deployment constraint described in the scenario. It mismatches the target platform, migration method, compute tier, or deployment model described in the scenario, so it would not satisfy the workload as stated.

Why B is Wrong: Filter by the Contact Info Information type- is an Azure data-platform feature, but it must match the workload model, service tier, migration path, or deployment constraint described in the scenario. It applies to a different Azure data-platform design than the one the question is testing.

Why C is Wrong: Filter by the CustomerlD column name. is an Azure data-platform feature, but it must match the workload model, service tier, migration path, or deployment constraint described in the scenario. It would solve a neighboring problem, but not the deployment, sizing, or migration requirement in this item.

Scenario: In the event of an Azure regional outage, ensure that the customers can access the PaaS solution with minimal downtime. The solution must provide automatic failover.

The auto-failover groups feature allows you to manage the replication and failover of a group of databases on a server or all databases in a managed instance to another region. It is a declarative abstraction on top of the existing active geo-replication feature, designed to simplify deployment and management of geo-replicated databases at scale. You can initiate failover manually or you can delegate it to the Azure service based on a user-defined policy.

The latter option allows you to automatically recover multiple related databases in a secondary region after a catastrophic failure or other unplanned event that results in full or partial loss of the SQL Database or SQL Managed Instance availability in the primary region.