Microsoft 70-475 Online Practice Questions and Exam Preparation

Microsoft 70-475 Online Questions & Answers

• Question 31:

  DRAG DROP

  You have a Microsoft Azure data factory named ADF1 that contains a pipeline named Pipeline1.

  You plan to automate updates to Pipeline1.

  You need to build the URL that must be called to update the pipeline from the REST API.

  How should you complete the URL? To answer, drag the appropriate URL elements to the correct locations. Each URL element may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to

  view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

  ---

  Box 1: management.azure.com

  The Create or Update Data Factory request may be constructed as follows (HTTPS is recommended): PUT https://management.azure.com/subscriptions/{SubscriptionID}/resourcegroups/{ResourceGroupName}/providers/

  Microsoft.DataFactory/datafactories/{DataFactoryName}?api-version={api-version}

  Box 2: Microsoft.DataFactory/datafactories/adf1

  The Microsoft Azure data factory is named ADF1.

  Box 3: datapipelines/pipelLine1

  The pipeline is named Pipeline1.

  References:

  https://docs.microsoft.com/en-us/rest/api/datafactory/v1/data-factory-data-factory

• Question 32:

  You are building an Azure Analysis Services cube.

  The source data for the cube is located on premises in a Microsoft SQL Server database.

  You need to ensure that the Azure Analysis Services service can access the source data.

  What should you deploy to your Azure subscription?

  A. Azure Data Factory
  B. a network gateway in Azure
  C. a data gateway in Azure
  D. a site-to-site VPN
  C. a data gateway in Azure

  ---

  Connecting to on-premises data sources from and Azure AS server require an On-premises gateway.

  

  References: https://azure.microsoft.com/en-in/blog/on-premises-data-gateway-support-for-azure-analysis-services/

• Question 33:

  Note: The 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 section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  Your company has multiple databases that contain millions of sales transactions.

  You plan to implement a data mining solution to identify purchasing fraud.

  You need to design a solution that mines 10 terabytes (TB) of sales date. The solution must meet the following requirements:

  Run the analysis to identify fraud once per week.

  Continue to receive new sales transactions while the analysis runs.

  Be able to stop computing services when the analysis is NOT running.

  Solution: You create a Cloudera Hadoop cluster on Microsoft Azure virtual machines.

  Does this meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Processing large amounts of unstructured data requires serious computing power and also maintenance effort. As load on computing power typically fluctuates due to time and seasonal influences and/or processes running on certain times, a cloud solution like Microsoft Azure is a good option to be able to scale up easily and pay only for what is actually used.

  Reference: http://blog.cloudera.com/blog/2016/02/how-to-install-cloudera-enterprise-on-microsoft-azure-part-1/

• Question 34:

  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 an Apache Spark system that contains 5 TB of data.

  You need to write queries that analyze the data in the system. The queries must meet the following requirements:

  Use static data typing.

  Execute queries as quickly as possible.

  Have access to the latest language features.

  Solution: You write the queries by using Python.

  A. Yes
  B. No
  B. No

• Question 35:

  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 an Apache Spark system that contains 5 TB of data.

  You need to write queries that analyze the data in the system. The queries must meet the following requirements:

  Use static data typing.

  Execute queries as quickly as possible.

  Have access to the latest language features.

  Solution: You write the queries by using Java.

  A. Yes
  B. No
  B. No

• Question 36:

  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 deployment that contains the following services: Azure Data Lake Azure Cosmos DB Azure Data Factory Azure SQL Database

  You load several types of data to Azure Data Lake.

  You need to load data from Azure SQL Database to Azure Data Lake.

  Solution: You use a stored procedure.

  Does this meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Note: You can use the Copy Activity in Azure Data Factory to copy data to and from Azure Data Lake Storage Gen1 (previously known as Azure Data Lake Store). Azure SQL database is supported as source. References: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-store

• Question 37:

  Overview:

  Litware, Inc. is a company that manufactures personal devices to track physical activity and other health-related data.

  Litware has a health tracking application that sends health-related data from a user's personal device to Microsoft Azure.

  Litware has three development and commercial offices. The offices are located in the United States, Luxembourg, and India.

  Litware products are sold worldwide. Litware has commercial representatives in more than 80 countries.

  Existing Environment:

  In addition to using desktop computers in all of the offices, Litware recently started using Microsoft Azure resources and services for both development and operations.

  Litware has an Azure Machine Learning solution.

  Litware recently extended its platform to provide third-party companies with the ability to upload data from devices to Azure. The data can be aggregated across multiple devices to provide users with a comprehensive view of their global

  health activity.

  While the upload from each device is small, potentially more than 100 million devices will upload data daily by using an Azure event hub. Each health activity has a small amount of data, such as activity type, start date/time, and end date/time.

  Each activity is limited to a total of 3 KB and includes a customer identification key.

  In addition to the Litware health tracking application, the users' activities can be reported to Azure by using an open API.

  The developers at Litware perform Machine Learning experiments to recommend an appropriate health activity based on the past three activities of a user.

  The Litware developers train a model to recommend the best activity for a user based on the hour of the day.

  Requirements:

  Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.

  Minimize the costs associated with transferring data from the event hub to Azure Storage.

  Litware identifies the following technical requirements:

  Data from the devices must be stored for three years in a format that enables the fast processing of date fields and filtering.

  The third-party companies must be able to use the Litware Machine Learning models to generate recommendations to their users by using a third-party application.

  Any changes to the health tracking application must ensure that the Litware developers can run the experiments without interrupting or degrading the performance of the production environment.

  Activity tracking data must be available to all of the Litware developers for experimentation. The developers must be prevented from accessing the private information of the users.

  When the Litware health tracking application asks users how they feel, their responses must be reported to Azure.

  Users report that when they access data that is more than one year old from a dashboard, the response time is slow.

  You need to resolve the issue that causes the slow response when visualizing older data.

  What should you do?

  A. Process the event hub data first, and then process the older data on demand.
  B. Process the older data on demand first, and then process the event hub data.
  C. Aggregate the older data by time, and then save the aggregated data to reference data streams.
  D. Store all of the data from the event hub in a single partition.
  C. Aggregate the older data by time, and then save the aggregated data to reference data streams.

• Question 38:

  You are designing a solution that will use Apache HBase on Microsoft Azure HDInsight.

  You need to design the row keys for the database to ensure that client traffic is directed over all of the nodes in the cluster.

  What are two possible techniques that you can use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. padding
  B. trimming
  C. hashing
  D. salting
  C. hashing
  D. salting

  ---

  There are two strategies that you can use to avoid hotspotting:

  *

  Hashing keys

  To spread write and insert activity across the cluster, you can randomize sequentially generated keys by hashing the keys, inverting the byte order. Note that these strategies come with trade-offs. Hashing keys, for example, makes table

  scans for key subranges inefficient, since the subrange is spread across the cluster.

  *

  Salting keys

  Instead of hashing the key, you can salt the key by prepending a few bytes of the hash of the key to the actual key.

  Note. Salted Apache HBase tables with pre-split is a proven effective HBase solution to provide uniform workload distribution across RegionServers and prevent hot spots during bulk writes. In this design, a row key is made with a logical key plus salt at the beginning. One way of generating salt is by calculating n (number of regions) modulo on the hash code of the logical row key (date, etc).

  Reference: https://blog.cloudera.com/blog/2015/06/how-to-scan-salted-apache-hbase-tables-with-region-specific-key-ranges-in-mapreduce/ http://maprdocs.mapr.com/51/MapR-DB/designing_row_keys_for_mapr_db_binary_tables.html

• Question 39:

  HOTSPOT

  You are planning a solution that will have multiple data files stored in Microsoft Azure Blob storage every hour.

  You have a large datacenter.

  You plan to track the hardware failure notifications that occur in the datacenter. You expect to collect approximated 2 TB of data each month.

  You need to recommend a solution that meets the following requirements:

  Operators must be informed by email as soon as a hardware failure occurs.

  All event data associated with a hardware failure must be preserved for 24 months.

  The solution must minimize costs.

  What should you identify? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  

  ---

  Box 1: Microsoft Azure Logic App

  With Azure Logic Apps and the Simple Mail Transfer Protocol (SMTP) connector, you can create automated tasks and workflows that send email from your SMTP account. You can also have other actions use the output from SMTP actions.

  Incorrect Answers:

  Notification Hubs: Notification Hubs is primarily designed to send notifications to mobile apps. It does not provide email or text message capabilities.

  Box 2: Microsoft Azure Data Lake

  A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question.

  References:

  https://searchaws.techtarget.com/definition/data-lake

• Question 40:

  You use Microsoft Azure Data Factory to orchestrate data movement and data transformation within Azure.

  You need to identify which data processing failures exceed a specific threshold.

  What are two possible ways to achieve the goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. View the Diagram tile on the Data Factory blade of the Azure portal.
  B. Set up an alert to send an email message when the number of failed validations is greater than the threshold.
  C. View the data factory metrics on the Data Factory blade of the Azure portal.
  D. Set up an alert to send an email message when the number of failed slices is greater than or equal to the threshold.
  B. Set up an alert to send an email message when the number of failed validations is greater than the threshold.
  C. View the data factory metrics on the Data Factory blade of the Azure portal.

  ---

  References: https://docs.microsoft.com/en-us/azure/azure-monitor/platform/alerts-metric