Microsoft Perform Data Engineering on Microsoft Azure HDInsight - 070-775 FREE EXAM DUMPS QUESTIONS & ANSWERS

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. The cluster will host a low-latency NoSQL database that uses the key/value pair format in a columnar model. The cluster has the following requirements:
Supports SSH access
Supports autosharding
Uses Azure Data Lake Store as the default storage
What should you do?

You have an Apache Spark cluster in Azure HDInsight.
You execute the following command.

What is the result of running the command?

You have an Apache Spark cluster in Azure HDInsight.
Users report that Spark jobs take longer than expected to complete.
You need to reduce the amount of time it takes for the Spark jobs to complete.
What should you do?

You use YARN to manage the resources for a Spark Thrift Server running on a Linux-based Apache Spark cluster in Azure HDInsight.
You discover that the cluster does not fully utilize the resources. You want to increase resource allocation.
You need to increase the number of executors and the allocation of memory to the Spark Thrift Server driver.
Which two parameters should you modify? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

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 dat
a. 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 event-based 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?

DRAG DROP
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 dat
a. 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 event-based 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 need to meet the following stream analytics requirements:
Send tuples to bolts in a random, round-robin sequence.
Send tuples to a bolt based on one or more fields in the tuple.
Which type of stream grouping should you use for each requirement? To answer, drag the appropriate grouping types to the correct requirements. Each grouping 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.

You have several Linux-based and Windows-based Azure HDInsight clusters. The clusters are indifferent Active Directory domains.
You need to consolidate system logging for all of the clusters into a single location. The solution must provide near real-time analytics of the log dat a.
What should you use?