Certifications: MapR®
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: MapR Certified Data Analyst Certification
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: MapR Certified Cluster Administrator
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- Total
Module : appox.
30 modules
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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.
- If you need regular updates than
please Subscribe Here
( MCSD V2 : MapR Certified Spark
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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