• Programming language is Scala (Previously they were asking Spark Java Questions as well, and now it is removed)
• 10% -20% Questions are based on Concepts mostly for Machine Learning
• 80% Questions are based on Programming with sample Data
• Quite complex programming questions, need to be answered in limited time. Its quite challenging
• GraphX questions are not being asked (As per learners feedback)
• Code snippet will be given to answer questions.
• In real exam question count varies between 60-80 questions.
• They ask questions from all 7 sections.
• File formats understanding is very important.
• Without going through all 220 questions from the HadoopExam simulator, it is very difficult to clear the exam.
• Best way to prepare for MapR Spark certification is use below three products.

1. Scala Professional Training
2. Spark Professional Training (In Scala)
3. MapR Certified Spark Certification Simulator (220+ Questions)

• If you have any doubt contact us using contact detail, mentioned on the top of the page. either email, WhatsApp Call, Direct Call etc.

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This entire package will prepare you for Spark Programming, Scala Programming and MapR Spark Scala Certification exam: Includes below three products (PACK3MAPRSPRKSCLTRN7777) Spark Professioanl  Training. with HadnsOn Session  Scala Professional Training with HandsOn Session  MapR Spark Scala Certifications To customize contact hadoopexam@gmail.com

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MCHD : MapR Hadoop Developer Certification MCHBD : MapR HBase Certification MCSD : MapR Spark Developer To customize contact hadoopexam@gmail.com

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•     1.1 Demonstrate how to create and use Resilient Distributed Datasets (RDDs)
•     1.2 Apply transformation to RDDs, such as map(), filter(), distinct(), reduceByKey
•     1.3 Apply actions to RDDs such as count(), collect(), reduce(func), take (n), foreach(func), first()
•     1.4 Demonstrate how and when to cache intermediate RDDs and persist
•     1.5 Identify the differences between Actions and Transformations

•     2.1 Describe how MapReduce jobs are executed and monitored in both MapReduce v.1 and in YARN
•     2.2 Define the function of SparkContext
•     2.3 Create a simple Spark application creating a SparkContext with a main method
•     2.4 Describe the differences between running Spark in the interactive shell vs. building a standalone application
•     2.5 Run a Spark application in modes including Local, Standalone, YARN, and Mesos

•     3.1 Define ways to create Pair RDDs from existing RDDs and from automatically loaded     data
•     3.2 Apply transformations on Pair RDDs including groupByKey and reduceByKey
•     3.3 Describe the differences between groupByKey and reduceByKey and how each     are used
•     3.4 Use transformations specific to two pPair RDDs, including joins
•     3.5 Apply actions on pPair RDDs
•     3.6 Control partitioning across nodes, including specifying the type of partitioner and number of partitions

•     4.1 Create DataFrames from existing RDDs, data sources, and using reflection
•     4.2 Demonstrate how to use DataFrame operations including domain methods and SQL
•     4.3 Demonstrate how to register a user-defined function
•     4.4 Define how to repartition DataFrames

•     5.1 Describe components of the Spark execution model such as stages, tasks, and jobs
•     5.2 Use Spark Web UI to monitor Spark applications
•     5.3 Debug and tune Spark application logic, as well as Spark configurations, including
•     optimizations, memory cache, and data locality

•     6.1 Describe Apache Spark Streaming architecture components and their purpose
•     6.2 Demonstrate how to use Spark Streaming to process streaming data
•     6.3 Demonstrate how to create DStreams using standard RDD operations and stateful operations
•     6.4 Demonstrate how to trigger computation using output operations such as saveAsTextFiles, foreachRDD, and others
•     6.5 Demonstrate how to process streaming data using Window operations including countByWindow, reduceByWindow, countByValueAndWindow, and others
•     6.6 Describe how Spark Streaming achieves fault tolerance

•     7.1 Use accumulators as atomic aggregates and broadcast variables as a way to share data
•     7.2 Differentiate between supervised and unsupervised machine learning
•     7.3 Identify use of classification, clustering, and recommendation machine learning libraries
•     7.4 Describe the basic process for machine learning using Spark

• Entire syllabus will be covered.
• Any future updates will be free on single and same machine.
• Solutions are already executed on Spark 1.6.
• Our technical expert regularly update the simulator based on learners feedback and their own research for specific product.
• It will help you gain confidence and reduce study time.
• Always updated and explanations of  wherever required.

Note : This product is tested only on Windows Operating System. IOS users are using this Wine Bottler to run exe.

We have training subscriber from TCS, IBM, INFOSYS, ACCENTURE, APPLE, HEWITT, Oracle , NetApp , Capgemini etc.