DP-300 Administering Relational Databases on Microsoft Azure Questions and Answers

Box 1: DTU

Scenario:

The 30 new databases must scale automatically.

Once all requirements are met, minimize costs whenever possible.

You can configure resources for the pool based either on the DTU-based purchasing model or the vCore-based purchasing model.

In short, for simplicity, the DTU model has an advantage. Plus, if you’re just getting started with Azure SQL Database, the DTU model offers more options at the lower end of performance, so you can get started at a lower price point than with vCore.

Box 2: An Azure SQL database elastic pool

Azure SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single server and share a set number of resources at a set price. Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database.

Box 1: Yes

DBAGroup1 is member of the Contributor role.

The Contributor role grants full access to manage all resources, but does not allow you to assign roles in Azure RBAC, manage assignments in Azure Blueprints, or share image galleries.

Box 2: No

Box 3: Yes

DBAGroup2 is member of the SQL DB Contributor role.

The SQL DB Contributor role lets you manage SQL databases, but not access to them. Also, you can't manage their security-related policies or their parent SQL servers. As a member of this role you can create and manage SQL databases.

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. 

To ensure that all queries executed against dbl are captured in the Query Store, you need to enable the Query Store feature for the database and set the query capture mode to ALL. The Query Store feature provides you with insight on query plan choice and performance for Azure SQL Database1. The query capture mode controls whether all queries or only a subset of queries are tracked2.

Here are the steps to enable the Query Store and set the query capture mode to ALL for the database dbl:

Using the Azure portal:

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

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

Click on Configure Query Store and turn on the Query Store switch.

In the Query Capture Mode dropdown, select All and click on Save.

Using Transact-SQL statements:

Connect to the Azure SQL Database server and the database dbl using SQL Server Management Studio or Azure Data Studio.

Run the following command to enable the Query Store for the database: ALTER DATABASE dbl SET QUERY_STORE = ON;

Run the following command to set the query capture mode to ALL for the database: ALTER DATABASE dbl SET QUERY_STORE (QUERY_CAPTURE_MODE = ALL);

These are the steps to ensure that all queries executed against dbl are captured in the Query Store.

There are CPU and Data I/O spikes for the PaaS prototype. Business Critical 4-vCore is needed.

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.

Scenario: Use a single dashboard to review security and audit data for all the PaaS databases.

With dashboards can bring together operational data that is most important to IT across all your Azure resources, including telemetry from Azure Log Analytics.

Note: Auditing for Azure SQL Database and Azure Synapse Analytics tracks database events and writes them to an audit log in your Azure storage account, Log Analytics workspace, or Event Hubs.

To enable change data capture (CDC) for db1, you need to run the stored procedure sys.sp_cdc_enable_db in the database context. CDC is a feature that records activity on a database when tables and rows have been modified1. CDC can be used for various scenarios, such as data synchronization, auditing, or ETL processes2.

Here are the steps to enable CDC 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: EXEC sys.sp_cdc_enable_db; GO

This command will enable CDC for the database and create the cdc schema, cdc user, metadata tables, and other system objects for the database3.

To verify that CDC is enabled for db1, you can query the is_cdc_enabled column in the sys.databases catalog view. The value should be 1 for db1.

These are the steps to enable CDC for db1

To enable page compression on the PK_SalesOrderHeader_SalesOrderlD clustered index of the SalesLT.SalesOrderHeader table in db1, you can use the following Transact-SQL script:

-- Connect to the Azure SQL database named db1

USE db1;

GO

-- Enable page compression on the clustered index

ALTER INDEX PK_SalesOrderHeader_SalesOrderlD ON SalesLT.SalesOrderHeader

REBUILD WITH (DATA_COMPRESSION = PAGE);

GO

This script will rebuild the clustered index with page compression, which can reduce the storage space and improve the query performance

The script solution consists of three parts:

The first part is USE db1; GO. This part connects to the Azure SQL database named db1, where the SalesLT.SalesOrderHeader table is located. The GO command separates the batches of Transact-SQL statements and sends them to the server.

The second part is ALTER INDEX PK_SalesOrderHeader_SalesOrderlD ON SalesLT.SalesOrderHeader REBUILD WITH (DATA_COMPRESSION = PAGE); GO. This part enables page compression on the clustered index named PK_SalesOrderHeader_SalesOrderlD, which is defined on the SalesLT.SalesOrderHeader table. The ALTER INDEX statement modifies the properties of an existing index. The REBUILD option rebuilds the index from scratch, which is required to change the compression setting. The DATA_COMPRESSION = PAGE option specifies that page compression is applied to the index, which means that both row and prefix compression are used. Page compression can reduce the storage space and improve the query performance by compressing the data at the page level. The GO command ends the batch of statements.

The third part is optional, but it can be useful to verify the compression status of the index. It is SELECT name, index_id, data_compression_desc FROM sys.indexes WHERE object_id = OBJECT_ID('SalesLT.SalesOrderHeader');. This part queries the sys.indexes catalog view, which contains information about the indexes in the database. The SELECT statement returns the name, index_id, and data_compression_desc columns for the indexes that belong to the SalesLT.SalesOrderHeader table. The OBJECT_ID function returns the object identification number for the table name. The data_compression_desc column shows the compression type of the index, which should be PAGE for the clustered index after the script is executed.

These are the steps of the script solution for enabling page compression on the clustered index of the SalesLT.SalesOrderHeader table in db1.

Box 1: Hash

Scenario:

Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.

A hash distributed table can deliver the highest query performance for joins and aggregations on large tables.

Box 2: Round-robin

Scenario:

You plan to create a promotional table that will contain a promotion ID. The promotion ID will be associated to a specific product. The product will be identified by a product ID. The table will be approximately 5 GB.

A round-robin table is the most straightforward table to create and delivers fast performance when used as a staging table for loads. These are some scenarios where you should choose Round robin distribution:

When you cannot identify a single key to distribute your data.

If your data doesn’t frequently join with data from other tables.

When there are no obvious keys to join.

To configure a disaster recovery solution for db1, you can use the failover groups feature of Azure SQL Database. Failover groups allow you to manage the replication and failover of a group of databases across different regions with the same connection strings1. You can also use active geo-replication as an alternative, but you will need to update the connection strings manually after a failover2.

Here are the steps to create a failover group for db1 with the secondary server in the West US 3 region:

Using the Azure portal:

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

Select Failover groups in the left menu and click on Add group.

Enter a name for the failover group and select West US 3 as the secondary region.

Click on Create a new server and enter the details for the secondary server, such as server name, admin login, password, and subscription.

Click on Select existing database(s) and choose db1 from the list of databases on the primary server.

Click on Configure failover policy and select the failover mode, grace period, and read-write failover endpoint mode according to your preferences.

Click on Create to create the failover group and start the replication of db1 to the secondary server.

Using PowerShell commands:

Install the Azure PowerShell module and log in with your Azure account.

Run the following command to create a new server in the West US 3 region: New-AzSqlServer -ResourceGroupName -ServerName -Location "West US 3" -SqlAdministratorCredentials $(New-Object -TypeName System.Management.Automation.PSCredential -ArgumentList "", $(ConvertTo-SecureString -String "" -AsPlainText -Force))

Run the following command to create a new failover group with db1: New-AzSqlDatabaseFailoverGroup -ResourceGroupName -ServerName -PartnerResourceGroupName -PartnerServerName -FailoverGroupName -Database db1 -FailoverPolicy Manual -GracePeriodWithDataLossHours 1 -ReadWriteFailoverEndpoint "Enabled"

You can modify the parameters of the command according to your preferences, such as the failover policy, grace period, and read-write failover endpoint mode.

These are the steps to create a failover group for db1 with the secondary server in the West US 3 region.

SQL injection attacks are a type of cyberattack that exploit a vulnerability in the application code that interacts with the database. An attacker can inject malicious SQL statements into the user input, such as a form field or a URL parameter, and execute them on the database server, resulting in data theft, corruption, or unauthorized access1.

To protect all the databases on sql37006S95 from SQL injection attacks, you need to follow some best practices for securing your application and database layers. Here are some of the recommended steps:

Use parameterized queries or stored procedures to separate the SQL code from the user input. This will prevent the user input from being interpreted as part of the SQL statement and avoid SQL injection23.

Validate and sanitize the user input before passing it to the database. This will ensure that the input conforms to the expected format and type, and remove any potentially harmful characters or keywords4.

Implement least privilege access for the database users and roles. This will limit the permissions and actions that the application can perform on the database, and reduce the impact of a successful SQL injection attack5.

Enable Advanced Threat Protection for Azure SQL Database. This is a feature that detects and alerts you of anomalous activities and potential threats on your database, such as SQL injection, brute force attacks, or unusual access patterns. You can configure the alert settings and notifications using the Azure portal or PowerShell.

These are some of the steps to protect all the databases on sql37006S95 from SQL injection attacks.

To generate an email alert to admin@contoso.com when CPU percentage utilization for db1 is higher than average, you can use the Azure portal to create an alert rule based on the CPU percentage metric. Here are the steps to do that:

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

Select Alerts in the Monitoring section and click on New alert rule.

In the Condition section, click Add and select the CPU percentage metric.

In the Configure signal logic page, set the threshold type to Dynamic. This will compare the current metric value to the historical average and trigger the alert when it deviates significantly1.

Set the operator to Greater than, the aggregation type to Average, the aggregation granularity to 1 minute, and the frequency of evaluation to 5 minutes.

Click Done to save the condition.

In the Action group section, click Create and enter a name and a short name for the action group.

In the Notifications section, click Add and select Email/SMS message/Push/Voice.

Enter admin@contoso.com in the Email field and click OK.

Click OK to save the action group.

In the Alert rule details section, enter a name and a description for the alert rule, choose a severity level, and make sure the rule is enabled.

Click Create alert rule to create the alert rule.

This alert rule will send an email to admin@contoso.com when the CPU percentage utilization for db1 is higher than average. You can also add other actions to the alert rule, such as calling a webhook or running an automation script

To configure your user account as the Azure AD admin for the server named sql3700689S, you can use the Azure portal or the Azure CLI. Here are the steps for both methods:

Using the Azure portal:

Go to the Azure portal and select SQL Server – Azure Arc.

Select the server named sql3700689S and click on Active Directory admin.

Click on Set admin and choose your user account from the list of Azure AD users.

Click on Select and then Save to confirm the change.

You can verify the Azure AD admin by clicking on Active Directory admin again and checking the current admin.

Using the Azure CLI:

Install the Azure CLI and log in with your Azure account.

Run the following command to get the object ID of your user account: az ad user show --id --query objectId -o tsv

Run the following command to set your user account as the Azure AD admin for the server: az sql server ad-admin create --server sql3700689S --object-id --display-name

You can verify the Azure AD admin by running the following command: az sql server ad-admin show --server sql3700689S

These are the steps to configure your user account as the Azure AD admin for the server named sql3700689S.

To configure sq1370O6895 to support the creation of new database users from Azure AD identities, you need to do the following steps:

Set up a Microsoft Entra tenant and associate it with your Azure subscription. You can use the Microsoft Entra portal or the Azure portal to create and manage your Microsoft Entra users and groups12.

Configure a Microsoft Entra admin for sq1370O6895. You can use the Azure portal or the Azure CLI to set a Microsoft Entra user as the admin for the server34. The Microsoft Entra admin can create other database users from Microsoft Entra identities5.

Connect to db1 using the Microsoft Entra admin account and run the following Transact-SQL statement to create a new database user from a Microsoft Entra identity: CREATE USER [Microsoft Entra user name] FROM EXTERNAL PROVIDER;6 You can replace the Microsoft Entra user name with the name of the user or group that you want to create in the database.

Grant the appropriate permissions to the new database user by adding them to a database role or granting them specific privileges. For example, you can run the following Transact-SQL statement to add the new user to the db_datareader role: ALTER ROLE db_datareader ADD MEMBER [Microsoft Entra user name];

These are the steps to configure sq1370O6895 to support the creation of new database users from Azure AD identities.

To configure high availability for dbl, you can use the failover groups feature of Azure SQL Database. Failover groups allow you to manage the replication and failover of a group of databases across different regions with the same connection strings1. You can choose all, or a subset of, user databases in a logical server to be replicated to another logical server in a different region. You can also specify the failover policy, such as manual or automatic, and the grace period for data loss.

Here are the steps to create a failover group for dbl:

Using the Azure portal:

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

Select Failover groups in the left menu and click on Add group.

Enter a name for the failover group and select a secondary region that is different from the primary region.

Click on Create a new server and enter the details for the secondary server, such as server name, admin login, password, and subscription.

Click on Select existing database(s) and choose dbl from the list of databases on the primary server.

Click on Configure failover policy and select the failover mode, grace period, and read-write failover endpoint mode according to your preferences.

Click on Create to create the failover group and start the replication of dbl to the secondary server.

Using PowerShell commands:

Install the Azure PowerShell module and log in with your Azure account.

Run the following command to create a new server in the secondary region: New-AzSqlServer -ResourceGroupName -ServerName -Location "" -SqlAdministratorCredentials $(New-Object -TypeName System.Management.Automation.PSCredential -ArgumentList "", $(ConvertTo-SecureString -String "" -AsPlainText -Force))

Run the following command to create a new failover group with dbl: New-AzSqlDatabaseFailoverGroup -ResourceGroupName -ServerName -PartnerResourceGroupName -PartnerServerName -FailoverGroupName -Database dbl -FailoverPolicy -GracePeriodWithDataLossHours -ReadWriteFailoverEndpoint ""

You can modify the parameters of the command according to your preferences, such as the failover policy, grace period, and read-write failover endpoint mode.

These are the steps to create a failover group for dbl

To ensure that any enhancements made to the Query Optimizer through patches are available to dbl and db2 on sql37006895, you need to enable the query optimizer hotfixes option for each database. This option allows you to use the latest query optimization improvements that are not enabled by default1. You can enable this option by using the ALTER DATABASE SCOPED CONFIGURATION statement2.

Here are the steps to enable the query optimizer hotfixes option for dbl and db2 on sql37006895:

Connect to sql37006895 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 commands for each database:

-- Switch to the database context

USE dbl;

GO

-- Enable the query optimizer hotfixes option

ALTER DATABASE SCOPED CONFIGURATION SET QUERY_OPTIMIZER_HOTFIXES = ON;

GO

Repeat the same commands for db2, replacing dbl with db2 in the USE statement.

To verify that the query optimizer hotfixes option is enabled for each database, you can query the sys.database_scoped_configurations catalog view. The value of the query_optimizer_hotfixes column should be 1 for both databases.

These are the steps to enable the query optimizer hotfixes option for dbl and db2 on sql37006895.

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.

Scenario: Authenticate database users by using Active Directory credentials.

The configuration steps include the following procedures to configure and use Azure Active Directory authentication.

Create and populate Azure AD.

Optional: Associate or change the active directory that is currently associated with your Azure Subscription.

Create an Azure Active Directory administrator. (Step 1)

Configure your client computers.

Create contained database users in your database mapped to Azure AD identities. (Step 2)

Connect to your database by using Azure AD identities. (Step 3)

Azure Database Migration service

Box 1: Premium 4-VCore

Scenario: Migrate the SERVER1 databases to the Azure SQL Database platform.

Minimize downtime during the migration of the SERVER1 databases.

Premimum 4-vCore is for large or business critical workloads. It supports online migrations, offline migrations, and faster migration speeds.

SalesSQLDb1 experiences performance issues that are likely due to out-of-date statistics and frequent blocking queries.

A: Use sys.dm_pdw_nodes_tran_locks instead of sys.dm_tran_locks from Azure Synapse Analytics (SQL Data Warehouse) or Parallel Data Warehouse.

E: Example:

The following query will show blocking information.

SELECT

t1.resource_type,

t1.resource_database_id,

t1.resource_associated_entity_id,

t1.request_mode,

t1.request_session_id,

t2.blocking_session_id

FROM sys.dm_tran_locks as t1

INNER JOIN sys.dm_os_waiting_tasks as t2

ON t1.lock_owner_address = t2.resource_address;

Note: Depending on the system you’re working with you can access these wait statistics from one of three locations:

sys.dm_os_wait_stats: for SQL Server

sys.dm_db_wait_stats: for Azure SQL Database

sys.dm_pdw_nodes_os_wait_stats: for Azure SQL Data Warehouse

Scenario: Authenticate database users by using Active Directory credentials.

(Create a new Azure SQL database named ResearchDB1 on a logical server named ResearchSrv01.)

Authenticate the user in SQL Database or SQL Data Warehouse based on an Azure Active Directory user:

CREATE USER [Fritz@contoso.com] FROM EXTERNAL PROVIDER;

Scenario: Business Requirements

Litware identifies business requirements include: meet an SLA of 99.99% availability for all Azure deployments.

Box 1: Cloud witness

If you have a Failover Cluster deployment, where all nodes can reach the internet (by extension of Azure), it is recommended that you configure a Cloud Witness as your quorum witness resource.

Box 2: Azure Basic Load Balancer

Microsoft guarantees that a Load Balanced Endpoint using Azure Standard Load Balancer, serving two or more Healthy Virtual Machine Instances, will be available 99.99% of the time.

Note: There are two main options for setting up your listener: external (public) or internal. The external (public) listener uses an internet facing load balancer and is associated with a public Virtual IP (VIP) that is accessible over the internet. An internal listener uses an internal load balancer and only supports clients within the same Virtual Network.

Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools.

For each Spark external table based on Parquet and located in Azure Storage, an external table is created in a serverless SQL pool database. As such, you can shut down your Spark pools and still query Spark external tables from serverless SQL pool.

Cloud Provider Infrastructure Logs.

Databricks logging allows security and admin teams to demonstrate conformance to data governance

standards within or from a Databricks workspace. Customers, especially in the regulated industries, also need records on activities like:

User access control to cloud data storage

Cloud Identity and Access Management roles

User access to cloud network and compute

Azure Databricks offers three distinct workloads on several VM Instances tailored for your data analytics workflow—the Jobs Compute and Jobs Light Compute workloads make it easy for data engineers to build and execute jobs, and the All-Purpose Compute workload makes it easy for data scientists to explore, visualize, manipulate, and share data and insights interactively.

CE: Estimate the vCores needed for the pool as follows:

For vCore-based purchasing model: MAX(,

A: Estimate the storage space needed for the pool by adding the number of bytes needed for all the databases in the pool.

Example:

Free space for the file in the below query result set will be returned by the FreeSpaceMB column.

SELECT DB_NAME() AS DbName,

name AS FileName,

type_desc,

size/128.0 AS CurrentSizeMB,

size/128.0 - CAST(FILEPROPERTY(name, 'SpaceUsed') AS INT)/128.0 AS FreeSpaceMB

FROM sys.database_files

WHERE type IN (0,1);

Step 1: On the master database, run CREATE LOGIN [App1] WITH PASSWORD = 'p@aaW0rd!'

Logins are server wide login and password pairs, where the login has the same password across all databases. Here is some sample Transact-SQL that creates a login:

CREATE LOGIN readonlylogin WITH password='1231!#ASDF!a';

You must be connected to the master database on SQL Azure with the administrative login (which you get from the SQL Azure portal) to execute the CREATE LOGIN command.

Step 2: On DatabaseA, run CREATE USER [App1] FROM LOGIN [App1]

Users are created per database and are associated with logins. You must be connected to the database in where you want to create the user. In most cases, this is not the master database. Here is some sample Transact-SQL that creates a user:

CREATE USER readonlyuser FROM LOGIN readonlylogin;

Step 3: On DatabaseA run ALTER ROLE db_datareader ADD Member [App1]

Just creating the user does not give them permissions to the database. You have to grant them access. In the Transact-SQL example below the readonlyuser is given read only permissions to the database via the db_datareader role.

EXEC sp_addrolemember 'db_datareader', 'readonlyuser';

Box 1: FactEvents

Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc.

Box 2: DimChannel

Dimension tables describe business entities – the things you model. Entities can include products, people, places, and concepts including time itself. The most consistent table you'll find in a star schema is a date dimension table. A dimension table contains a key column (or columns) that acts as a unique identifier, and descriptive columns.

Box 3: DimEvent

The performance capacity of a query is determined by the user's resource class.

Smaller resource classes reduce the maximum memory per query, but increase concurrency.

Larger resource classes increase the maximum memory per query, but reduce concurrency.

Scenario: Contoso requirements for the sales transaction dataset include:

Implement a surrogate key to account for changes to the retail store addresses.

A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the

table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.

The lifecycle management policy lets you:

Delete blobs, blob versions, and blob snapshots at the end of their lifecycles

Scenario: The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.

Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Scenario: Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.

To monitor the performance of a database in Azure SQL Database and Azure SQL Managed Instance, start by monitoring the CPU and IO resources used by your workload relative to the level of database performance you chose in selecting a particular service tier and performance level.

To query the data in your Hadoop data source, you must define an external table to use in Transact-SQL queries. The following steps describe how to configure the external table.

Step 1: Create a master key on database.

1. Create a master key on the database. The master key is required to encrypt the credential secret.

(Create a database scoped credential for Azure blob storage.)

Step 2: Create an external data source for Azure Blob storage.

2. Create an external data source with CREATE EXTERNAL DATA SOURCE..

Step 3: Create an external file format to map the parquet files.

3. Create an external file format with CREATE EXTERNAL FILE FORMAT.

Step 4. Create an external table FactSalesOrderDetails

4. Create an external table pointing to data stored in Azure storage with CREATE EXTERNAL TABLE.

https://www.techtarget.com/searchitoperations/tip/Easily-transfer-VMs-to-the-cloud-with-Microsoft-Azure-Migrate

Answer as in image below.

The diagnostics log outputs tempDB contention details. You can use the information as the starting point for troubleshooting.

You can use the Intelligent Insights performance diagnostics log of Azure SQL Database to troubleshoot performance issues.