The 'Spark Training- Post Test' assesses understanding of Apache Spark, focusing on core concepts like Spark SQL, DataFrame schemas, and in-memory computing. It evaluates entry-level skills necessary for efficient data processing and analytics, making it crucial for learners aiming to excel in data-intensive environments.
Spark can run on the top of Hadoop
Spark can process data stored in HDFS
Spark can use Yarn as resource management layer
All of the above
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TRUE
FALSE
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In-memory computation
Lazy evaluations
Fault Tolerance
All of the above
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TRUE
FALSE
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Scheduling
Distributing data across cluster
Monitoring data across cluster
All of the above
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4
3
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Batch Processing
Stream Processing
Graph Processing
Interactive Processing
All of the above
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Java
Scala
Python
All of the above
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Count
First
Take(n)
Collect
All the above
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Df.printSchema()
Df.show()
Df.take
Printschema
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Map
Filter
Join
All of the above
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RDD is a database
RDD is a distributed data structure
RDD is a programming paradigm
None
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10 times faster
20 times faster
100 times faster
200 times faster
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Yarn
Mesos
Standalone
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11
55
66
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Standalone Cluster Manager
Mesos
YARN
All of the above
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78
44
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64MB
128MB
256MB
None of the above
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More than one
Only one
Not specific
None of the above
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Immutable
Mutable
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MEMORY_ONLY
MEMORY_AND_DISK
DISK_ONLY
MEMORY_ONLY_SER
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Immutable
Recomputable
Fault-tolerant
All of the above
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List(one, two, a, b)
List(one, two)
List(a,b)
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Red
Green
Error
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Yarn
Mesos
Standalone cluster manager
All of the above
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Eagerly
Lazily
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Spark 1.6
Spark 1.4.0
Spark 2.0
Spark 1.1
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Avro
Textfile
Parquet
JSON
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Scala
Java
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3
2
1
None of the above
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Var myVar : Int=0;
Val myVal: Int=1;
Both A and B
None
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Hello
Pretty
Error
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Select
Where
Filter
Both a and b
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True
False
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TRUE
FALSE
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Range
Partitioner
HashPartitioner
None of the above
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Mydf.distinct()
Mydf.distinct
Mydf.distinct.count()
Not supported
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Topic
Messages
Chunks
Broker
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Take(n)
Take
Show()
Count
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Both are data processing engines
Both work on YARN
Both have their own file system
Both are open source
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Takes RDD as input and produces one or more RDD as output.
Returns final result of RDD computations.
The ways to send result from executors to the driver
None of the above
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Row-oriented
Tuple-oriented
Column-oriented
None of the mentioned
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DataSet
RDD
DataStream
Block
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Mydf.tordd
Mydf.rdd
Not supported
None of the above
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SparkContext (sc)
SparkSession (spark)
Both a and b
None of the above
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Error
44
40
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All the transformations and actions are lazily evaluated
Execution starts with the call of Action
Execution starts with the call of Transformation
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The data type of the variable has to be mentioned explicitly
The type of the variable is determined by looking at its value.
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