Microsoft DP-200 Exam Questions Instant Access

Question 1

What should you include in the Data Factory pipeline for Race Central?

Aa copy activity that uses a stored procedure as a source

Ba copy activity that contains schema mappings

Ca delete activity that has logging enabled

Da filter activity that has a condition

Answer : B

Scenario:

An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.

The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.

You can copy data to or from Azure Cosmos DB (SQL API) by using Azure Data Factory pipeline.

Column mapping applies when copying data from source to sink. By default, copy activity map source data to sink by column names. You can specify explicit mapping to customize the column mapping based on your need. More specifically, copy activity:

Read the data from source and determine the source schema

Use default column mapping to map columns by name, or apply explicit column mapping if specified.

Write the data to sink

References:

https://docs.microsoft.com/en-us/azure/data-factory/copy-activity-schema-and-type-mapping

Question 2

You have a data warehouse in Azure Synapse Analytics.

You need to ensure c rest.

What should you enable?

ATransparent Data Encryption (TDE)

BSecure transfer required

CAlways Encrypted for all columns

DAdvanced Data Security for this database

Answer : A

Question 3

On which data store you configure TDE to meet the technical requirements?

ACosmos DB

BSQL Data Warehouse

CSQL Database

Answer : B

Scenario: Transparent data encryption (TDE) must be enabled on all data stores, whenever possible.

The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.

Question 4

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this scenario, you will NOT be able to return to it. As a result, these

questions will not appear in the review screen.

You have an Azure SQL database named DB1 that contains a table named Table1. Table1 has a field named Customer_ID that is varchar(22).

You need to implement masking for the Customer_ID field to meet the following requirements:

The first two prefix characters must be exposed.

The last four prefix characters must be exposed.

All other characters must be masked.

Solution: You implement data masking and use a credit card function mask.

Does this meet the goal?

AYes

BNo

Answer : B

We must use Custom Text data masking, which exposes the first and last characters and adds a custom

padding string in the middle.

References:

https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started

Question 5

You need to ensure that phone-based poling data can be analyzed in the PollingData database.

How should you configure Azure Data Factory?

AUse a tumbling schedule trigger

BUse an event-based trigger

CUse a schedule trigger

DUse manual execution

Answer : C

When creating a schedule trigger, you specify a schedule (start date, recurrence, end date etc.) for the trigger, and associate with a Data Factory pipeline.

Scenario:

All data migration processes must use Azure Data Factory

All data migrations must run automatically during non-business hours

References:

https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-schedule-trigger

Question 6

You manage a solution that uses Azure HDInsight clusters.

You need to implement a solution to monitor cluster performance and status.

Which technology should you use?

AAzure HDInsight .NET SDK

BAzure HDInsight REST API

CAmbari REST API

DAzure Log Analytics

EAmbari Web UI

Answer : E

Ambari is the recommended tool for monitoring utilization across the whole cluster. The Ambari dashboard shows easily glanceable widgets that display metrics such as CPU, network, YARN memory, and HDFS disk usage. The specific metrics shown depend on cluster type. The ''Hosts'' tab shows metrics for individual nodes so you can ensure the load on your cluster is evenly distributed.

The Apache Ambari project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. Ambari provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.

References:

https://azure.microsoft.com/en-us/blog/monitoring-on-hdinsight-part-1-an-overview/

https://ambari.apache.org/

Question 7

You develop data engineering solutions for a company.

You must integrate the company's on-premises Microsoft SQL Server data with Microsoft Azure SQL

Database. Data must be transformed incrementally.

You need to implement the data integration solution.

Which tool should you use to configure a pipeline to copy data?

AUse the Copy Data tool with Blob storage linked service as the source

BUse Azure PowerShell with SQL Server linked service as a source

CUse Azure Data Factory UI with Blob storage linked service as a source

DUse the .NET Data Factory API with Blob storage linked service as the source

Answer : C

The Integration Runtime is a customer managed data integration infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.

A linked service defines the information needed for Azure Data Factory to connect to a data resource. We have three resources in this scenario for which linked services are needed:

On-premises SQL Server

Azure Blob Storage

Azure SQL database

Note: Azure Data Factory is a fully managed cloud-based data integration service that orchestrates and automates the movement and transformation of data. The key concept in the ADF model is pipeline. A pipeline is a logical grouping of Activities, each of which defines the actions to perform on the data contained in Datasets. Linked services are used to define the information needed for Data Factory to connect to the data resources.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-sql-azure-adf

Microsoft Implementing an Azure Data Solution DP-200 Exam Questions