Microsoft 70-475 Online Practice Questions and Exam Preparation

Microsoft 70-475 Online Questions & Answers

• Question 101:

  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 the others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You have a Microsoft Azure subscription that includes Azure Data Lake and Cognitive Services.

  An administrator plans to deploy an Azure Data Factory.

  You need to ensure that the administrator can create the data factory.

  Solution: You add the user to the Owner role.

  Does this meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  To create Data Factory instances, the user account that you use to sign in to Azure must be a member of the contributor or owner role, or an administrator of the Azure subscription.

  References: https://docs.microsoft.com/en-us/azure/data-factory/concepts-roles-permissions

• Question 102:

  You have an Apache Hadoop system that contains 5 TB of data.

  You need to create queries to analyze the data in the system. The solution must ensure that the queries execute as quickly as possible.

  Which language should you use to create the queries?

  A. Apache Pig
  B. Java
  C. Apache Hive
  D. MapReduce
  D. MapReduce

• Question 103:

  You are designing a solution based on the lambda architecture.

  You need to recommend which technology to use for the serving layer.

  What should you recommend?

  A. Kafka
  B. Apache Hadoop
  C. Azure Cosmos DB
  D. Apache Storm
  C. Azure Cosmos DB

  ---

  Azure Cosmos DB can be used to implement a lambda architecture on the Azure platform. The serving layer is an Azure Cosmos DB database with collections for the master dataset and computed batch view.

  References: https://docs.microsoft.com/en-us/azure/cosmos-db/lambda-architecture

• Question 104:

  HOTSPOT

  You have a Microsoft Azure SQL data warehouse named DW1.

  A department in your company creates an Azure SQL database named DB1. DB1 is a data mart.

  Each night, you need to insert new rows into 9.000 tables in DB1 from changed data in DW1. The solution must minimize costs.

  What should you use to move the data from DW1 to DB1, and then to import the changed data to DB1? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  

  ---

  Box 1: Azure Data Factory

  Use the Copy Activity in Azure Data Factory to move data to/from Azure SQL Data Warehouse.

  Box 2: The BULK INSERT statement

  Reference: https://docs.microsoft.com/en-us/azure/data-factory/data-factory-azure-sql-data-warehouse-connector

• Question 105:

  You have a Microsoft Azure Data Factory pipeline.

  You discover that the pipeline fails to execute because data is missing.

  You need to rerun the failure in the pipeline.

  Which cmdlet should you use?

  A. Set-AzureAutomationJob
  B. Resume-AzureDataFactoryPipeline
  C. Resume-AzureAutomationJob
  D. Set-AzureDataFactorySliceStatus
  B. Resume-AzureDataFactoryPipeline

  ---

  The Resume-AzureDataFactoryPipeline cmdlet resumes the specified pipeline that is currently in suspended state in an Azure data factory.

  Reference: https://github.com/Azure/azure-docs-powershell/blob/master/azureps-cmdlets-docs/ResourceManager/AzureRM.DataFactories/v0.9.8/Resume-AzureDataFactoryPipeline.md

• Question 106:

  You have an application that displays from a Microsoft Azure SQL database. The database contains credit card numbers.

  You need to ensure that the application only displays the last four digits of each credit card number when a credit card number is returned from a query. The solution must NOT require any changes to the data in the database.

  What should you use?

  A. Dynamic Data Masking
  B. cell-level security
  C. Transparent Data Encryption (TDE)
  D. row-level security
  A. Dynamic Data Masking

  ---

  Dynamic Data Masking can be used to hide or obfuscate sensitive data, by controlling how the data appears in the output of database queries. Dynamic Data Masking rules can be defined on particular columns, indicating how the data in those columns will appear when queried. There are no physical changes to the data in the database itself; the data remains intact and is fully available to authorized users or applications. Database operations remain unaffected, and the masked data has the same data type as the original data, so DDM can often be applied without making any changes to database procedures or application code.

  References: https://blogs.technet.microsoft.com/dataplatforminsider/2016/01/25/use-dynamic-data-masking-to-obfuscate-your-sensitive-data/

• Question 107:

  HOTSPOT

  You plan to implement a Microsoft Azure Data Factory pipeline. The pipeline will have custom business logic that requires a custom processing step.

  You need to implement the custom processing step by using C#.

  Which interface and method should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  

  ---

  References: https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-factory/v1/data-factory-use-custom-activities.md

• Question 108:

  DRAG DROP

  Overview:

  Relecloud is a social media company that processes hundreds of millions of social media posts per day and sells advertisements to several hundred companies. Relecloud has a Microsoft SQL Server database named DB1 that stores

  information about the advertisers. DB1 is hosted on a Microsoft Azure virtual machine.

  Relecloud has two main offices. The offices are located in San Francisco and New York City.

  The offices connect to each other by using a site-to-site VPN. Each office connects directly to the Internet.

  Relecloud modifies the pricing of its advertisements based on trending topics. Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame. The highest trending topics generate the

  highest advertising revenue.

  Relecloud wants to deliver reports to the advertisers by using Microsoft Power BI. The reports will provide real-time data on trending topics, current advertising rates, and advertising costs for a given month. Relecloud will analyze the trending

  topics data, and then store the data in a new data warehouse for ad-hoc analysis. The data warehouse is expected to grow at a rate of 1 GB per hour or 8.7 terabytes (TB) per year. The data will be retained for five years for the purpose of

  long-term trending.

  Requirements:

  Management at Relecloud must be able to view which topics are trending to adjust advertising rates in near real-time.

  Relecloud plans to implement a new streaming analytics platform that will report on trending topics.

  Relecloud plans to implement a data warehouse named DB2.

  Relecloud identifies the following technical requirements:

  Social media data must be analyzed to identify trending topics in real-time.

  The use of Infrastructure as a Service (IaaS) platforms must minimized, whenever possible.

  The real-time solution used to analyze the social media data must support scaling up and down without service interruption.

  Relecloud identifies the following technical requirements for the advertisers:

  The advertisers must be able to see only their own data in the Power BI reports.

  The advertisers must authenticate to Power BI by using Azure Active Directory (Azure AD) credentials.

  The advertisers must be able to leverage existing Transact-SQL language knowledge when developing the real-time streaming solution.

  Members of the internal advertising sales team at Relecloud must be able to see only the sales date of the advertisers to which they are assigned.

  The internal Relecloud advertising sales team must be prevented from inserting, updating, and deleting rows for the advertisers to which they are not assigned.

  The internal Relecloud advertising sales team must be able to use a text file to update the list of advertisers, and then to upload the file to Azure Blob storage.

  Relecloud identifies the following requirements for DB1:

  Data generated by the streaming analytics platform must be stored in DB1.

  The user names of the advertisers must be mapped to CustomerID in a table named Table2.

  The advertisers in DB1 must be stored in a table named Table1 and must be refreshed nightly.

  The user names of the employees at Relecloud must be mapped to EmployeeID in a table named Table3.

  Relecloud identifies the following requirements for DB2:

  DB2 must have minimal storage costs.

  DB2 must run load processes in parallel.

  DB2 must support massive parallel processing.

  DB2 must be able to store more than 40 TB of data.

  DB2 must support scaling up and down, as required.

  Data from DB1 must be archived in DB2 for long-term storage.

  All of the reports that are executed from DB2 must use aggregation.

  Users must be able to pause DB2 when the data warehouse is not in use.

  Users must be able to view previous versions of the data in DB2 by using aggregates.

  Relecloud identifies the following requirements for extract, transformation, and load (ETL):

  Data movement between DB1 and DB2 must occur each hour.

  An email alert must be generated when a failure of any type occurs during ETL processing.

  Sample code and data:

  You execute the following code for a table named rls_table1.

  

  You use the following code to create Table1.

  create table table1 (customerid int, salespersonid int ... ) Go

  The following is a sample of the streaming data.

  

  You need to implement a solution that meets the data refresh requirement for DB1.

  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Azure Data Factory can be used to orchestrate the execution of stored procedures. This allows more complex pipelines to be created and extends Azure Data Factory's ability to leverage the computational power of SQL Data Warehouse.

  From scenario:

  Relecloud has a Microsoft SQL Server database named DB1 that stores information about the advertisers. DB1 is hosted on a Microsoft Azure virtual machine.

  Relecloud identifies the following requirements for DB1:

  Data generated by the streaming analytics platform must be stored in DB1.

  The advertisers in DB1 must be stored in a table named Table1 and must be refreshed nightly.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-move-sql-server-virtual-machine

• Question 109:

  You have a Microsoft Azure Machine Learning Solution that contains several Azure Data Factory pipeline jobs.

  You discover that the jobs for a dataset named CustomerSalesData fails.

  You resolve the issue that caused the job to fail.

  You need to rerun the slices for CustomerSalesData.

  What should you do?

  A. Run the Set-AzureRMDataFactorySliceStatus cmdlet and specify the–Status Retry parameter.
  B. Run the Set-AzureRMDataFactorySliceStatus cmdlet and specify the–Status PendingExecution parameter.
  C. Run the Resume-AzureRMDataFactoryPipeline cmdlet and specify the–Status Retry parameter.
  D. Run the Resume-AzureRMDataFactoryPipeline cmdlet and specify the–Status PendingExecution parameter.
  B. Run the Set-AzureRMDataFactorySliceStatus cmdlet and specify the–Status PendingExecution parameter.

• Question 110:

  You are designing an Internet of Thing: (IoT) solution intended to identify trends. The solution requires the real-time analysis of data originating from sensors. The results of the analysis will be stored in a SQL database.

  You need to recommend a data processing solution that uses the Transact-SQL language.

  Which data processing solution should you recommend?

  A. Microsoft Azure Stream Analytics
  B. Microsoft Azure HDInsight Spark clusters
  C. Microsoft Azure Event Hubs
  D. Microsoft Azure HDInsight Hadoop clusters
  A. Microsoft Azure Stream Analytics

  ---

  For your Internet of Things (IoT) scenarios that use Event Hubs, Azure Stream Analytics can serve as a possible first step to perform near real-time analytics on telemetry data. Just like Event Hubs, Steam Analytics supports the streaming of millions of event per second. Unlike a standard database, analysis is performed on data in motion. This streaming input data can also be combined with reference data inputs to perform lookups or do correlation to assist in unlocking business insights. It uses a SQL-like language to simplify the analysis of data inputs and detect anomalies, trigger alerts or transform the data in order to create valuable outputs.

  Reference: https://www.linkedin.com/pulse/getting-started-azure-iot-services-stream-analytics-rob-tiffany