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NEW QUESTION NO: 5
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.
You need to recommend a data handling solution to support the planned changes to the dashboard.
What is the best recommendation to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
A. anonymization
B. encryption
C. obfuscation
D. compression
Answer: C
Explanation/Reference:
Explanation:
From scenario: 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.
The developers must be prevented from accessing the private information of the users.
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.
Reference: https://blogs.technet.microsoft.com/dataplatforminsider/2016/01/25/use-dynamic-data- masking-to-obfuscate-your-sensitive-data/

NEW QUESTION NO: 6
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.
You extend the dashboard of the health tracking application to summarize fields across several users.
You need to recommend a file format for the activity data in Azure that meets the technical requirements.
What is the best recommendation to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
A. ORC
B. TSV
C. CSV
D. JSON
E. XML
Answer: E
Explanation/Reference:
Explanation:
From scenario: 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.
XML is a good file format for filtering and processing of fields.

NEW QUESTION NO: 7
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
Answer: C,D
Explanation/Reference:
Explanation:
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

NEW QUESTION NO: 8
You need to recommend a platform architecture for a big data solution that meets the following requirements:
Supports batch processing

Provides a holding area for a 3-petabyte (PB) dataset

Minimizes the development effort to implement the solution

Provides near real-time relational querying across a multi-terabyte (TB) dataset

Which two platform architectures should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. a Microsoft Azure SQL data warehouse
B. a Microsoft Azure HDInsight Hadoop cluster
C. a Microsoft SQL Server database
D. a Microsoft Azure HDInsight Storm cluster
E. Microsoft Azure Table Storage
Answer: A,E
Explanation/Reference:
Explanation:
A: Azure SQL Data Warehouse is a SQL-based, fully-managed, petabyte-scale cloud data warehouse. It's highly elastic, and it enables you to set up in minutes and scale capacity in seconds. Scale compute and storage independently, which allows you to burst compute for complex analytical workloads, or scale down your warehouse for archival scenarios, and pay based on what you're using instead of being locked into predefined cluster configurations-and get more cost efficiency versus traditional data warehouse solutions.
E: Use Azure Table storage to store petabytes of semi-structured data and keep costs down. Unlike many data stores-on-premises or cloud-based-Table storage lets you scale up without having to manually shard your dataset. Perform OData-based queries.
Reference:
https://azure.microsoft.com/en-us/services/sql-data-warehouse/
https://azure.microsoft.com/en-us/services/storage/tables/

NEW QUESTION NO: 9
You have a Microsoft Azure subscription that contains an Azure Data Factory pipeline.
You have an RSS feed that is published on a public website.
You need to configure the RSS feed as a data source for the pipeline.
Which type of linked service should you use?
A. web
B. OData
C. Azure Search
D. Azure Data Lake Store
Answer: A
Explanation/Reference:
Explanation:
Reference: https://docs.microsoft.com/en-us/azure/data-factory/data-factory-web-table-connector

NEW QUESTION NO: 10
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. Apache Storm
B. Kafka
C. Microsoft Azure DocumentDB
D. Apache Hadoop
Answer: C
Explanation/Reference:
Explanation:
The Serving Layer is a bit more complicated in that it needs to be able to answer a single query request against two or more databases, processing platforms, and data storage devices. Apache Druid is an example of a cluster-based tool that can marry the Batch and Speed layers into a single answerable request.
Reference: https://en.wikipedia.org/wiki/Lambda_architecture

NEW QUESTION NO: 11
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 create a query that identifies the trending topics.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value 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:

Answer: 

Explanation/Reference:
Explanation:
From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp
FROM SensorReadings
TIMESTAMP BY time
GROUP BY sensorId, SlidingWindow(second, 5)
HAVING MIN(temp) > 75
Box 3: SlidingWindow
Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.
Reference: https://blogs.technet.microsoft.com/machinelearning/2015/06/01/the-azure-stream-analytics- query-language/

NEW QUESTION NO: 12
DRAG DROP
You manage a Microsoft Azure HDInsight Hadoop cluster. All of the data for the cluster is stored in Azure Premium Storage.
You need to prevent all users from accessing the data directly. The solution must allow only the HDInsight service to access the data.
Which five 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:

Answer: 

Explanation/Reference:
Explanation:
Example: scenario where a customer wishes to explore a move from an existing Hortonworks (HDP) cluster to an Azure HDInsight (HDI) cluster.
With the target Storage account in place, I can now configure the HDP cluster to connect to it. The easiest way I have found to do this is using Ambari:
Login to the Ambari portal and navigate to the (default) dashboard

Select HDFS from the left-hand navigation

On the resulting HDFS page, click on the Configs tab

On the resulting page, select the Advanced option

Scroll down and expand the Custom core-site node

Select Add Property... from the bottom of the expanded node

Enter fs.azure.account.key.<account name>.blob.core.windows.net, substituting the name of the

Storage account for <account name>, as the Name of the property
Enter the Account key as the Value of the property

Click the Add button and verify the new property appears under the Custom core-site node

Locate the notification bar at the top of the current page

Click the Save button on the notification bar to push changes to the cluster

Follow any remaining prompts to complete the Save process

Once the Save process is completed, Ambari will indicate a restart of some services is required. Click

the Restart button and Restart All Affected from the resulting drop-down. Follow any remaining prompts and monitor the process until it is successfully completed.
Reference: https://blogs.msdn.microsoft.com/data_otaku/2016/12/09/pushing-data-from-a-hortonworks- cluster-to-an-azure-hdinsight-cluster/

NEW QUESTION NO: 13
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
Answer: B
Explanation/Reference:
Explanation:
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

NEW QUESTION NO: 14
DRAG DROP
You work for a telecommunications company that uses Microsoft Azure Stream Analytics.
You have data related to incoming calls.
You need to group the data in the following ways:
Group A: Every five minutes for a duration of five minutes

Group B: Every five minutes for a duration of 10 minutes

Which type of window should you use for each group? To answer, drag the appropriate window types to the correct groups. Each window type 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:

Answer: 

Explanation/Reference:
Explanation:
Group A: Tumbling
Tumbling Windows define a repeating, non-overlapping window of time.
Group B: Hopping
Like Tumbling Windows, Hopping Windows move forward in time by a fixed period but they can overlap with one another.
Reference: https://blogs.technet.microsoft.com/machinelearning/2015/06/01/the-azure-stream-analytics- query-language/

NEW QUESTION NO: 15
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.
You need to recommend a data transfer solution to support the business goals.
What should you recommend?
A. Configure the health tracking application to cache data locally for 24 hours.
B. Configure the health tracking application to aggregate activities in blocks of 128 KB.
C. Configure the health tracking application to cache data locally for 12 hours.
D. Configure the health tracking application to aggregate activities in blocks of 64 KB.
Answer: D
Explanation/Reference:
Explanation:
From scenario:
Minimize the costs associated with transferring data from the event hub to Azure Storage.
Each activity is limited to a total of 3 KB and includes a customer identification key.