Q. ____________ is a distributed machine learning framework on top of Spark.

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Q. ________ is a distributed graph processing framework on top of Spark.

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Q. GraphX provides an API for expressing graph computation that can model the __________ abstraction.

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Q. Spark architecture is ___________ times as fast as Hadoop disk-based Apache Mahout and even scales better than Vowpal Wabbit.

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Q. Users can easily run Spark on top of Amazon’s __________

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Q. Spark runs on top of ___________ a cluster manager system which provides efficient resource isolation across distributed applications.

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Q. Which of the following can be used to launch Spark jobs inside MapReduce?

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Q. Which of the following language is not supported by Spark?

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Q. Spark is packaged with higher level libraries, including support for _________ queries.

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Q. Spark includes a collection over ________ operators for transforming data and familiar data frame APIs for manipulating semi-structured data.

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Q. Spark is engineered from the bottom-up for performance, running ___________ faster than Hadoop by exploiting in memory computing and other optimizations.

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Q. Spark powers a stack of high-level tools including Spark SQL, MLlib for _________

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Q. Apache Flume 1.3.0 is the fourth release under the auspices of Apache of the so-called ________ codeline.

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Q. ___________ was created to allow you to flow data from a source into your Hadoop environment.

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Q. A ____________ is an operation on the stream that can transform the stream.

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Q. A number of ____________ source adapters give you the granular control to grab a specific file.

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Q. ____________ is used when you want the sink to be the input source for another operation.

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Q. ___________ is where you would land a flow (or possibly multiple flows joined together) into an HDFS-formatted file system.

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Q. ____________ sink can be a text file, the console display, a simple HDFS path, or a null bucket where the data is simply deleted.

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Q. Flume deploys as one or more agents, each contained within its own instance of _________

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