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Certifications: MapR® Certfications Preparation Kits

MCS DV2 : MapR®  Certified Spark (Scala)  Developer V2

  • Total Scenario : 210+
  • Price : 2999INR or  $69                    
MCHD : MapR® Hadoop Developer Certification

  • Total Scenario :  600+
  • Video URL : Check Here
  • Price : 2900INR or  $65     
MCHBD : MapR®  HBase Developer Certification

  • Total Scenario : 285+
  • Video URL : Check Here
  • Price : 2900INR or  $65   

MCDA : MapR Certified Data Analyst Certification

  • Total Scenario :  In Progress
  • Video URL : Check Here
  • Price : NA
                          
MCCA : MapR Certified Cluster Administrator
  • Total Scenario : In Progress
  • Video URL : Check Here
  • Price : NA

  • Total Module : appox. 30 modules
  • Video URL : Check Here
  • Price : 4999INR or  $119 

About MapR MCSD V2 : MapR® Certified Spark Developer V2:
Total 210+ Solved Questions : Latest questions as per new syllabus

MapR is one of the popular vendor for BigData solutions and they have entire ecosystem created for various BigData engineering, Machine Learning, IOT and Data Science problems. Yes, they have leveraged all the available open source products and optimized those products for efficiency and make it more user friendly. However, to prove that you have good knowledge of MapR products, you need to certify yourself in those particular products. We have been continuously receiving request from our huge loyal learners that they wish to go for MapR Spark certification as well. Many of them already certified different products using HadoopExam certification preparation material. Our dedicated technical team had done lot of research and feedback from existing learner as well as who appeared in real exam, we have created a bundle of 210+ MapR Spark Scala (Version 2) Certification material. Main challenge of Spark  certification is the version and complex programming questions with sample data, which version are you using to prepare the exam. MapR Spark V2 is completely new certification which ask/test questions based on Spark 2.x version. MapR itself started supporting Spark 2.0 version on their products. No doubt since last 3 years Spark is one of the dominant technology for BigData, Machine Learning and Data Science. MapR Spark Previous version (Spark 1.6) certification has been retired. Even good news is that Programming in Spark 2 is much easier then Spark 1. Spark 2 specially improved their Spark SQL API and using that API they have enhanced Spark SQL, Spark Machine Learning, Spark GraphFrame and Spark Structured Streaming. Currentlyu we have 210+ relevant questions covering the entire syllabus of MapR Spark Scala (Version2) certification. Please keep in mind in real exam 80% questions are based on program. As Spark is framework written in Scala had very matured programming language. As per current market trend it is one of the technology which is in top trending list. We have seen most of our learners are Java, C++ programmer, Python Programmer, Data Analyst willing to become data scientist, BigData Engineer etc. which proves that demand is very high for Spark certified developer. Please subscribe for MapR Spark Scala Certification (Version 2) with 210+ questions You can also get bundle of more than one relevant product.


Some important points about MapR Spark Scala Certification (Version 2)
  • Programming language is Scala (Previously they were asking Spark Java Questions as well, and now it is removed)
  • Machine Learning has been completely removed from the syllabus. So programmer can now focus on Programming and concepts.
  • 80% Questions are based on Programming with sample Data
  • Quite complex programming questions, need to be answered in limited time. Its quite challenging
  • GraphX/GraphFrame questions are not being asked (As per learners feedback as well as it is not part of the syllabus)
  • Code snippet will be given to answer questions (In some cases, where expected program output is required).
  • In real exam question count varies between 60-80 questions.
  • They ask questions from all 16 sections.
  • Usually 4 questions from each section.
  • Understanding of DatraFrame/DataSet is very critical
  • Structured streaming many questions are expected
  • Without going through all 210 questions from the HadoopExam simulator, it is very difficult to clear the exam. 
  • Expertise : Once you complete all 210 questions then you would become expert as well in Spark programming (We highly recommed you bundle this certification material with below products)
  • MapR Spark Certification Simulator + Spark Professional Training (Scala) + Spark 2.x SQL Training(37 Hands On Exercises)  + Scala Training
  • 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 (Version 2) exam: Includes below three products (PACK4MAPRSPRKV2SCLTRN3377)
  1. Spark Professioanl  Training. with HadnsOn Session 
  2. Spark 2.x SQL Training (Scala)
  3. Scala Professional Training with HandsOn Session 
  4. MapR Spark Scala Certifications

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Get all 3 MapR Certification Simulators  (50% + 30% Discount) : PACK3MAPRCERTSIM3377
  1. MCHD : MapR Hadoop Developer Certification
  2. MCHBD : MapR HBase Certification
  3. MCSD : MapR Spark Developer Version2

 
  1. MCHD : MapR Hadoop Developer Certification
  2. MCHBD : MapR HBase Certification
  3. MCSD : MapR Spark Developer
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Required Skills for MapR Certified Spark Scala Developer (MCSD)

1. Create Datasets and DataFrames

1.1 Define Spark Components :  In this section you need to learn which all are the core component for Spark to run your program in distributed manner for example Driver Program, SparkSession, Cluster Manager, Worker Node, Executor, Task, Various Deployment modes etc.

1.2 Define Data Sources, Structures, and Schemas: For example Hive, HBase, MapR-XD, MapR-DB, JDBC, File System (csv, json, Parquet, Sequence Files, Protocol Buffer, Data from Cloud like AWS S3 etc)

1.3 Create Datasets and DataFrames : Creation of DataFrame and Datasets using various sources as above. Its properties like Dataset Immutable, Distributed tables with named columns, DataFrame with Unknown Data Types, Fault Tolerant  DataFrame, DataFrame with the Schema, what is the relation between Dataset[Row] and DataFrame.

1.4 Convert DataFrames into Datasets : Understanding Case Classes, Reflections, Convert DataFrame to Dataset, What is RDD, DataFrame and Dataset and which one to be preferred. Defining custom schema to the data. Loading/Saving Parquet files etc.

2. Working with Datasets

2.1 Apply Operations on Datasets : You must be able to apply various operations on the Datasets to achieve final results (However, it is quite easy compare to RDD API, in most of the cases you would be using Spark SQL API or queries). Understanding of Transformations and Actions, which one to use correctly. Certainly many programming questions will appear based on this. You should be able to answer questions from the Dataset similar to below (You should be able to select correct programming snippet). If you are already worked on RDD API then this API, you will feel very easy.
  • What are the top 5 trainings based on the most subscribers?
  • What are the top 5 subscriptions?
  • What are the subscriptions are not preferred? 

2.2 Cache Datasets: Understand various modes are available to cache the data, when it is advantageous to cache the data. Should i cache it or persist on the disk, various confusing scenario will be given. A program snippet would be given and they ask, which Dataset should be cached. Whether you should use unpersist or not?, application crash scenario in case of not enough cache is available.

2.3 Create User Defined Functions : How to create and Use user Defined functions Using Scala Lambda syntax to answer questions.

2.4 Partitioning datasets: How does partitioning of Data impact on parallelism, increasing and decreasing the partitioning. How does spark.sql.shuffle.partitions affect, how to change the parameters configuration, getting partition size like ds.rdd.partitions.size() and whether repartitioning would help like using ds.repartion(numberOfPartitions)

3. Monitor Spark Applications

3.1 Define the Spark Program Lifecycle : Understanding a various phases of a Spark program like using input Datasets, using Transformations, Caching/Persisting Data, then applying action to kick off the calculations. What is the Lazy transformations, what is spark-submit, when SparkSession should be created, Using Cluster Resources etc.

3.2 Use SparkSession : SparkSession is a starting point for the Spark Application, how to use SparkSession to create and manipulate Datasets.
 
3.3 Launch Spark Applications: Various applications deployment modes as below
  • Local/Standalone/YARN/Mesos
  • Difference between Client and Cluster Modes
  • Mesos Coarse-grained and Fine Grained Mode
  • Dynamic Resource Allocations
  • spark-submit : various optional arguments to submit the applications.
  • Logical plan, DAG (Direct Acyclic Graph), Physical execution plan
  • What is stages, Jobs and Tasks
  • Various Spark Web UIs for Job, Stage, History details and debugging the submitted jobs.
  • MapR Control System
  • Serialization formats, Tuning memory usage, Data Skew impact on parallelism, Where is the performance problem. 
  • Log Files
                       
4. Spark Streaming

4.1 Spark Streaming Architecture : Streaming use cases, What is structured streaming, how to leverage Spark SQL for structured streaming, Various streaming modes like Complete, Append and Update etc.

4.2 Create DStreams and Spark Streaming Applications

4.3 Apply Operations on DStreams : Aggregate functions of Streaming Data, Applying SQL operations on Streaming Data,

4.4 Define Windowed Operations : Window, Sliding interval , Sliding Window operations

4.5 Create Streaming Applications that are Fault Tolerant : Checkpointing, Logging, WAL (Write Ahead Logs)


Features of the MCSD V2 (Spark) Certification Simulator
  • Entire syllabus will be covered.
  • Any future updates will be free on single and same machine for same exam.
  • Solutions are already executed on Spark 2.x.
  • 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.

* Please read faq section carefully


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

Books on Spark or PDF to read : Machine Learning with Spark, Fast Data Processing with Spark (Second edition), Mastering Apache Spark, Learning Hadoop 2, Learning Real-time Processing with Spark Streaming, Apache Spark in Action, Apache Spark CookBook, Learning Spark, Advanced Analytics with Spark Download

Note : You can choose more than one product to have custome package created from below and send email to hadoopexam@gmail.com to get discount

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