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NEW QUESTION: 1
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 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 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?
A. Apache Spark
B. Apache Hadoop
C. Apache Storm
D. Apache HBase
Answer: C
Explanation:
References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-storm-overview

NEW QUESTION: 2
A customer needs an HPE Synergy compute module to host a SQL database for the customer's business-critical CRM solution
What makes the HPE Synergy 620 Gen9 Compute Module a good choice for this customer?
A. It is designed for mission critical workloads and provides of high amount of memory for the SQL database.
B. it is a half-height module, so a Synergy Frame can hold 12 of these modules, meeting the scale out needs of a SQL database.
C. It provides the highest number of mezzanine cards, meeting the SQL database's needs for high bandwidth.
D. It supports a higher density of local HDDs than other Synergy Compute Modules.
meeting the storage needs.
Answer: D

NEW QUESTION: 3
仕訳が最終モードで作成されるときに、補助元帳仕訳にどのような順序を割り当てることができますか？
A. レポートシーケンス
B. アカウンティングシーケンス
C. 仕訳入力シーケンス
D. ドキュメントシーケンス
Answer: B

NEW QUESTION: 4

A. Option D
B. Option B
C. Option C
D. Option A
Answer: B