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Category: 70-775

Briefing 70-775: Perform Data Engineering on Microsoft Azure HDInsight

Which type of cluster should you identify?

Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training data. You plan to automate the process of adding more data
to the training models and to constantly tune the models by using the additional data, including data that is
collected in near real-time. The system will be used to analyze event data gathered from many different
sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate
predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from Facebook, which are eventbased only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a
hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is
retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types for
the various tasks associated to the processing pipeline.
You are designing the real-time portion of the input stream processing. The input will be a continuous stream of
data and each record will be processed one at a time. The data will come from an Apache Kafka producer.
You need to identify which HDInsight cluster to use for the final processing of the input data. This will be used to
generate continuous statistics and real-time analytics. The latency to process each record must be less than
one millisecond and tasks must be performed in parallel.
Which type of cluster should you identify?

What should you create?

Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training data. You plan to automate the process of adding more data
to the training models and to constantly tune the models by using the additional data, including data that is
collected in near real-time. The system will be used to analyze event data gathered from many different
sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate
predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from Facebook, which are eventbased only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a
hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is
retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types for
the various tasks associated to the processing pipeline.
You are planning a storage strategy for a large amount of analytic data used for the crime data analytics
system. The initial data load involves over 100 billion records, and more than two billion records will be added
daily.
You already created an Apache Hadoop cluster in HDInsight premium.
You need to implement the storage strategy to meet the following requirements:
The storage capacity must support 50 TB.
The storage must be optimized for Hadoop.
The data must be stored in its native format.
Enterprise-level security based on Active Directory must be supported.
What should you create?

Which function should you use?

Note: This question is part of a series of questions that use the same scenario. For your convenience,
the scenario is repeated in each question. Each question presents a different goal and answer choices,
but the text of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training data. You plan to automate the process of adding more data
to the training models and to constantly tune the models by using the additional data, including data that is
collected in near real-time. The system will be used to analyze event data gathered from many different
sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate
predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from Facebook, which are eventbased only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a
hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is
retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types for
the various tasks associated to the processing pipeline.
You plan to consolidate all of the streams into a single timeline, even though none of the streams report events
at the same interval.
You need to aggregate the data from the feeds to alight with the time interval stream. The result must be the
sum of all the values for each key within a 10 second interval, with the keys being the hashtags.
Which function should you use?

You need to deploy an HDInsight cluster that will have …

Note: This question is part of a series of questions that use the same or similar answer choices. An answer
choice may be correct for more than one question in the series. Each question is independent of the other
questions in this series. Information and details provided in a question apply only to that question.
You need to deploy an HDInsight cluster that will have a custom Apache Ambari configuration. The cluster will
be joined to a domain and must perform the following:Fast data analytics and cluster computing by using in-memory processing
Interactive queries and micro-batch stream processing
What should you do?

You need to deploy an HDInsight cluster that will provi…

Note: This question is part of a series of questions that use the same or similar answer choices. An answer
choice may be correct for more than one question in the series. Each question is independent of the other
questions in this series. Information and details provided in a question apply only to that question.
You need to deploy an HDInsight cluster that will provide in-memory processing, interactive queries, and microbatch stream processing. The cluster has the following requirements:
Uses Azure Data Lake Store as the primary storage
Can be used by HDInsight applications
What should you do?

You need to deploy an enterprise data warehouse that wi…

Note: This question is part of a series of questions that use the same or similar answer choices. An answer
choice may be correct for more than one question in the series. Each question is independent of the other
questions in this series. Information and details provided in a question apply only to that question.
You need to deploy an enterprise data warehouse that will support in-memory analytics. The data warehouse
must support connections that use the Microsoft Hive ODBC Driver and Beeline. The data warehouse will be
managed by using Apache Amrabi only.
What should you do?

You need to deploy a NoSQL database to an HDInsight cluster

Note: This question is part of a series of questions that use the same or similar answer choices. An answer
choice may be correct for more than one question in the series. Each question is independent of the other
questions in this series. Information and details provided in a question apply only to that question.
You need to deploy a NoSQL database to an HDInsight cluster. You will manage the server that host the
database by using Remote Desktop. The database must use the key/value pair format in a columnar model.
What should you do?

Which three actions should you perform in sequence?

DRAG DROP
Note: This question is part of a series of questions that use the same scenario. For your convenience, thescenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster
has 24 processor cores and 512 GB of memory.
The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)

The architecture will be used by the following users:
Support analysts who run applications that will use REST to submit Spark jobs.
Business analysts who use JDBC and ODBC client applications from a real-time view. The business
analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced
by subsequent queries.
Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of
the notebooks must support native interpreters for data sources that are batch processed. The serving layer
queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the
data sources, which allow the data analysts to use Spark SQL.
The data sources in the batch layer share a common storage container. The following data sources are used:
Hive for sales data
Apache HBase for operations data
HBase for logistics data by using a single region server
The business analysts require queries to monitor the sales data. The queries must be faster and more
interactive than the batch layer queries.
You need to create a new infrastructure to support the queries. The solution must ensure that you can tune the
cache policies of the queries.
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:

Which technology should you implement?

Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster
has 24 processor cores and 512 GB of memory.
The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)

The architecture will be used by the following users:
Support analysts who run applications that will use REST to submit Spark jobs.
Business analysts who use JDBC and ODBC client applications from a real-time view. The business
analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced
by subsequent queries.
Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of
the notebooks must support native interpreters for data sources that are batch processed. The serving layer
queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the
data sources, which allow the data analysts to use Spark SQL.
The data sources in the batch layer share a common storage container. The following data sources are used:
Hive for sales data
Apache HBase for operations data
HBase for logistics data by using a single region server
You need to ensure that the support analysts can develop embedded analytics applications by using the least
amount of development effort.
Which technology should you implement?

Which configuration settings should you modify to allev…

Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster
has 24 processor cores and 512 GB of memory.The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)

The architecture will be used by the following users:
Support analysts who run applications that will use REST to submit Spark jobs.
Business analysts who use JDBC and ODBC client applications from a real-time view. The business
analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced
by subsequent queries.
Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of
the notebooks must support native interpreters for data sources that are batch processed. The serving layer
queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the
data sources, which allow the data analysts to use Spark SQL.
The data sources in the batch layer share a common storage container. The following data sources are used:
Hive for sales data
Apache HBase for operations data
HBase for logistics data by using a single region server
The business analysts report that they experience performance issues when they run the monitoring queries.
You troubleshoot the performance issues and discover that the intermediate tables generated when the
analysts run the queries cause pressure for the Java Virtual Machine (JVM) garbage collection per job.
Which configuration settings should you modify to alleviate the performance issues?


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