Microsoft DP-203 Data Engineering on Microsoft Azure Exam Practice Test

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.

Watermark Delay indicates the delay of the streaming data processing job.

There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:

• Not enough processing resources in Stream Analytics to handle the volume of input events. To scale up resources, see Understand and adjust Streaming Units.
• Not enough throughput within the input event brokers, so they are throttled. For possible solutions, see Automatically scale up Azure Event Hubs throughput units.
• Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.

SQL Database 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

Hash-distribution improves query performance on large fact tables.

Graphical user interface, text, application, email Description automatically generated

Box 1: 0

The YearlyIncome column is of the money data type.

The Default masking function: Full masking according to the data types of the designated fields

• Use a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).

Box 2: the values stored in the database

Users with administrator privileges are always excluded from masking, and see the original data without any mask.

Box 1: Hash

Consider using a hash-distributed table when:

The table size on disk is more than 2 GB.

The table has frequent insert, update, and delete operations.

Box 2: Clustered columnstore

Clustered columnstore tables offer both the highest level of data compression and the best overall query performance.

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

Graphical user interface, text, application Description automatically generated

Box 1: MAX

The first step on the query finds the maximum time stamp in 10-minute windows, that is the time stamp of the last event for that window. The second step joins the results of the first query with the original stream to find the event that match the last time stamps in each window.

Query:

WITH LastInWindow AS

(

SELECT

MAX(Time) AS LastEventTime

FROM

Input TIMESTAMP BY Time

GROUP BY

TumblingWindow(minute, 10)

)

SELECT

Input.License_plate,

Input.Make,

Input.Time

FROM

Input TIMESTAMP BY Time

INNER JOIN LastInWindow

ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10

AND Input.Time = LastInWindow.LastEventTime

Box 2: TumblingWindow

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

Box 3: DATEDIFF

DATEDIFF is a date-specific function that compares and returns the time difference between two DateTime fields, for more information, refer to date functions.

Text Description automatically generated

Box 1: PIVOT

PIVOT rotates a table-valued expression by turning the unique values from one column in the expression into multiple columns in the output. And PIVOT runs aggregations where they're required on any remaining column values that are wanted in the final output.

Append Mode: Only new rows appended in the result table since the last trigger are written to external storage. This is applicable only for the queries where existing rows in the Result Table are not expected to change.

https://docs.databricks.com/getting-started/sp ark/streaming.html

D:\mudassar\Untitled.jpg

Answer as below

Answer:

Answer is below

https://docs.microsoft.com/en-us/azure/architecture/data-guide /technology-choices/stream-processing

The Delta provides the ability to infer the schema for data input which further reduces the effort required in managing the schema changes. The Slowly Changing Data(SCD) Type 2 records all the changes made to each key in the dimensional table. These operations require updating the existing rows to mark the previous values of the keys as old and then inserting new rows as the latest values. Also, Given a source table with the updates and the target table with dimensional data, SCD Type 2 can be expressed with the merge.

Example:

// Implementing SCD Type 2 operation using merge function

customersTable

as("customers")

merge(

stagedUpdates.as("staged_updates"),

"customers.customerId = mergeKey")

whenMatched("customers.current = true AND customers.address <> staged_updates.address")

updateExpr(Map(

"current" -> "false",

"endDate" -> "staged_updates.effectiveDate"))

whenNotMatched()

insertExpr(Map(

"customerid" -> "staged_updates.customerId",

"address" -> "staged_updates.address",

"current" -> "true",

"effectiveDate" -> "staged_updates.effectiveDate",

"endDate" -> "null"))

execute()

}

Box 1: CREATE EXTERNAL TABLE

An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.

Syntax:

CREATE EXTERNAL TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name }

( [ ,...n ] )

WITH (

LOCATION = 'folder_or_filepath',

DATA_SOURCE = external_data_source_name,

FILE_FORMAT = external_file_format_name

Box 2. OPENROWSET

When using serverless SQL pool, CETAS is used to create an external table and export query results to Azure Storage Blob or Azure Data Lake Storage Gen2.

Example:

AS

SELECT decennialTime, stateName, SUM(population) AS population

FROM

OPENROWSET(BULK 'https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release/us_population_county/year=*/*.parquet ',

FORMAT='PARQUET') AS [r]

GROUP BY decennialTime, stateName

GO

All file formats have different performance characteristics. For the fastest load, use compressed delimited text files.

You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.

See below answer.

Table Description automatically generated

Box 1: PARTITION

RANGE RIGHT FOR VALUES is used with PARTITION.

Part 2: [TransactionDateID]

Partition on the date column.

Example: Creating a RANGE RIGHT partition function on a datetime column

The following partition function partitions a table or index into 12 partitions, one for each month of a year's worth of values in a datetime column.

CREATE PARTITION FUNCTION [myDateRangePF1] (datetime)

AS RANGE RIGHT FOR VALUES ('20030201', '20030301', '20030401',

'20030501', '20030601', '20030701', '20030801',

'20030901', '20031001', '20031101', '20031201');

For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributed databases.

Example: Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.

Box 1: (CLUSTERED COLUMNSTORE INDEX

CLUSTERED COLUMNSTORE INDEX

Columnstore indexes are the standard for storing and querying large data warehousing fact tables. This index uses column-based data storage and query processing to achieve gains up to 10 times the query performance in your data warehouse over traditional row-oriented storage. You can also achieve gains up to 10 times the data compression over the uncompressed data size. Beginning with SQL Server 2016 (13.x) SP1, columnstore indexes enable operational analytics: the ability to run performant real-time analytics on a transactional workload.

Note: Clustered columnstore index

A clustered columnstore index is the physical storage for the entire table.

Diagram Description automatically generated

To reduce fragmentation of the column segments and improve performance, the columnstore index might store some data temporarily into a clustered index called a deltastore and a B-tree list of IDs for deleted rows. The deltastore operations are handled behind the scenes. To return the correct query results, the clustered columnstore index combines query results from both the columnstore and the deltastore.

Box 2: HASH([ProductKey])

A hash distributed table distributes rows based on the value in the distribution column. A hash distributed table is designed to achieve high performance for queries on large tables.

Choose a distribution column with data that distributes evenly

A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.

This type of tracking may be used for one or two columns in a dimension table. It is not common to use it for many members of the same table. It is often used in combination with Type 1 or Type 2 members.

Graphical user interface, application, email Description automatically generated

Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require Data Factory customers to trigger

pipelines based on events happening in storage account, such as the arrival or deletion of a file in Azure Blob Storage account.

Graphical user interface, text, application, table Description automatically generated

Box 1: Round-robin

Round-robin tables are useful for improving loading speed.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month.

Box 2: Hash

Hash-distributed tables improve query performance on large fact tables.

Graphical user interface, text, application, table Description automatically generated

Box 1: Create table

Scenario: Load the sales transaction dataset to Azure Synapse Analytics

Box 2: RANGE RIGHT FOR VALUES

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values).

FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.

Scenario: Load the sales transaction dataset to Azure Synapse Analytics.

Contoso identifies the following requirements for the sales transaction dataset:

• Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
• Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
• Implement a surrogate key to account for changes to the retail store addresses.
• Ensure that data storage costs and performance are predictable.
• Minimize how long it takes to remove old records.

Graphical user interface, text Description automatically generated

Box 1: Configure Evegent Hubs partitions

Scenario: Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.

Event Hubs is designed to help with processing of large volumes of events. Event Hubs throughput is scaled by using partitions and throughput-unit allocations.

Event Hubs traffic is controlled by TUs (standard tier). Auto-inflate enables you to start small with the minimum required TUs you choose. The feature then scales automatically to the maximum limit of TUs you need, depending on the increase in your traffic.

Box 2: An Azure Data Lake Storage Gen2 account

Scenario: Ensure that the data store supports Azure AD-based access control down to the object level.

Azure Data Lake Storage Gen2 implements an access control model that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs).

Scenario: 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.

Scenario: Purge Twitter feed data records that are older than two years.

Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.

Graphical user interface, application Description automatically generated

Scenario: Identify a process to ensure that changes to the ingestion and transformation activities can be version-controlled and developed independently by multiple data engineers.

Step 1: Create a repository and a main branch

You need a Git repository in Azure Pipelines, TFS, or GitHub with your app.

Step 2: Create a feature branch

Step 3: Create a pull request

Step 4: Merge changes

Merge feature branches into the main branch using pull requests.

Step 5: Publish changes

Box 1: Sales date

Scenario: Contoso requirements for data integration include:

• Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Box 2: An Azure Synapse Analytics Dedicated SQL pool

Scenario: Contoso requirements for data integration include:

• Ensure that data storage costs and performance are predictable.

The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).

Dedicated SQL pool (formerly SQL DW) stores data in relational tables with columnar storage. This format significantly reduces the data storage costs, and improves query performance.

Synapse analytics dedicated sql pool

Scenario: Allow Contoso users to use PolyBase in an Azure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own Azure AD credentials.

Box 1: CREATE EXTERNAL DATA SOURCE

External data sources are used to connect to storage accounts.

Box 2: CREATE EXTERNAL FILE FORMAT

CREATE EXTERNAL FILE FORMAT creates an external file format object that defines external data stored in Azure Blob Storage or Azure Data Lake Storage. Creating an external file format is a prerequisite for creating an external table.

Box 3: CREATE EXTERNAL TABLE AS SELECT

When used in conjunction with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into a table within the SQL pool. In addition to the COPY statement, external tables are useful for loading data.

Graphical user interface, text, application, chat or text message Description automatically generated

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: Set the distribution column to the sales date.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.

Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In other words, virtual network rule applies at the serverlevel, not at the database-level.

References:

https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overvi ew

Scenario: All cloud data must be encrypted at rest and in transit.

Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.

References:

https://docs.microsoft. com/en-us/azure/sql-database/sql-database-security-overview

Guarantee Stream Analytics job reliability during service updates

Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model.

Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure

Box 1: Self-hosted integration runtime

A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.

Box 2: Schedule trigger

Schedule every 8 hours

Box 3: Copy activity

Scenario:

• Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
• Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.