Microsoft DP-300 Administering Relational Databases on Microsoft Azure Exam Practice Test

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.

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.

Box 1: The server, the elastic pool, and the database

Senario:

SalesSQLDb1 is in an elastic pool named SalesSQLDb1Pool.

Litware technical requirements include: all SQL Server and Azure SQL Database metrics related to CPU and storage usage and limits must be analyzed by using Azure built-in functionality.

Box 2: Azure Event hubs

Scenario: Migrate ManufacturingSQLDb1 to the Azure virtual machine platform.

Event hubs are able to handle custom metrics.

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.

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.

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.

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

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.

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

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.

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 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 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

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 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 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 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: 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

Columnstore tables and indexes are always stored with columnstore compression. You can further reduce the size of columnstore data by configuring an additional compression called archival compression.

Note: Columnstore — The columnstore index is also logically organized as a table with rows and columns, but the data is physically stored in a column-wise data format.

Answer as in image below.

Anomalies can be identified by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics

Box 1: 0

Custom String : Masking method that exposes the first and last letters and adds a custom padding string in the middle. prefix,[padding],suffix

Box 2: xxx-xxx

Box 3: 5

Read onlyReadWrite

Instead restore Database1 from Server1 to the Server2 by using the RESTORE Transact-SQL command and the REPLACE option.

Note: REPLACE should be used rarely and only after careful consideration. Restore normally prevents accidentally overwriting a database with a different database. If the database specified in a RESTORE statement already exists on the current server and the specified database family GUID differs from the database family GUID recorded in the backup set, the database is not restored. This is an important safeguard.