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

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: 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.

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.

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.

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: SalesSQLDb1 uses database firewall rules and contained database users.

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)

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.

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.

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.

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.

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

Automating Azure SQL DB index and statistics maintenance using Azure Automation:

1. Create Azure automation account (Step 1)

2. Import SQLServer module (Step 2)

3. Add Credentials to access SQL DB

This will use secure way to hold login name and password that will be used to access Azure SQL DB

4. Add a runbook to run the maintenance (Step 3)

Steps: 1. Click on "runbooks" at the left panel and then click "add a runbook"

2. Choose "create a new runbook" and then give it a name and choose "Powershell" as the type of the runbook and then click on "create"

5. Schedule task (Step 4)

Steps:1. Click on Schedules2. Click on "Add a schedule" and follow the instructions to choose existing schedule or create a new schedule.

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.

Step 1: Create a column master key

Create a column master key metadata entry before you create a column encryption key metadata entry in the database and before any column in the database can be encrypted using Always Encrypted.

Step 2: Create a column encryption key.

Step 3: Encrypt the Salary column by using the randomized encryption type.

Randomized encryption uses a method that encrypts data in a less predictable manner. Randomized encryption is more secure, but prevents searching, grouping, indexing, and joining on encrypted columns.

Note: A column encryption key metadata object contains one or two encrypted values of a column encryption key that is used to encrypt data in a column. Each value is encrypted using a column master key.

Read onlyReadWrite

Instead deploy an Azure SQL database that uses the Business Critical service tier and Availability Zones.

Note: Premium and Business Critical service tiers leverage the Premium availability model, which integrates compute resources (sqlservr.exe process) and storage (locally attached SSD) on a single node. High availability is achieved by replicating both compute and storage to additional nodes creating a three to four-node cluster.

By default, the cluster of nodes for the premium availability model is created in the same datacenter. With the introduction of Azure Availability Zones, SQL Database can place different replicas of the Business Critical database to different availability zones in the same region. To eliminate a single point of failure, the control ring is also duplicated across multiple zones as three gateway rings (GW).

Azure HDInsight offers pre-made, monitoring dashboards in the form of solutions that can be used to monitor the workloads running on your clusters. There are solutions for Apache Spark, Hadoop, Apache Kafka, live long and process (LLAP), Apache HBase, and Apache Storm available in the Azure Marketplace.

Note: With Azure HDInsight you can set up Azure Monitor alerts that will trigger when the value of a metric or the results of a query meet certain conditions. You can condition on a query returning a record with a value that is greater than or less than a certain threshold, or even on the number of results returned by a query. For example, you could create an alert to send an email if a Spark job fails or if a Kafka disk usage becomes over 90 percent full.

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.

Box 1: Yes

We see: Tuning option: Create index ON

CREATE INDEX - Identifies indexes that may improve performance of your workload, creates indexes, and automatically verifies that performance of queries has improved.

Box 2: No

Box 3: Yes

FORCE LAST GOOD PLAN (automatic plan correction) - Identifies Azure SQL queries using an execution plan that is slower than the previous good plan, and queries using the last known good plan instead of the regressed plan.

Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics support dynamic data masking. Dynamic data masking limits sensitive data exposure by masking it to non-privileged users.

The Credit card masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.

Example:

XXXX-XXXX-XXXX-1234

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