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Cloudera CCA175: CCA Spark and Hadoop Developer Exam

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How to Prepare For CCA-175: CCA Spark and Hadoop Developer Exam

Preparation Guide for CCA-175: CCA Spark and Hadoop Developer Exam


CCA-175 is an Apache Hadoop with Apache Spark and Scala Training and Certification Program. The major objective of this program is to help Hadoop developers to establish a formidable command, over the current traditional Hadoop Development protocols with advanced tools and operational procedures. CCA 175 Spark and Hadoop Developer is one of the well recognized Big Data certification. This scenario based certification exam demands basic programming using Python or Scala along with Spark and other Big Data technologies.

Cloudera’s CCA Spark and Hadoop Developer (CCA175) exam validates the candidate’s ability to employ various Big Data tools such as Hadoop, Spark, Hive, Impala, Sqoop, Flume, Kafka, etc to solve hands-on problems.

CCA175 requires good knowledge and hands-on experience with technologies such as Hadoop, HDFS, Spark, Scala, PySpark, Sqoop, Hive, Flume, Kafka and Avro. If you enjoy setting goals and working towards them you cam properly learn and practice these technologies. Unlike other Spark certification exams, CCA175 tests not just on Spark but also on other Big Data technologies.

To become a CCA-175 certifies developer, you should have the following:

  • Get Hands-on Experience as to how you can become Spark Application Developers
  • Become master at working with Spark DataFrames, HiveQL, and Spark SQL
  • Understand how to control importing and exporting of Data in Spark through Apache Sqoop in the exact format that is needed
  • Learn all Spark RDDs Transformations and Actions needed to analyze Big Data
  • Become absolutely ready for the Cloudera Spark CCA 175 Certification Exam

Introduction to CCA-175: CCA Spark and Hadoop Developer Exam

This comprehensive exam covers all aspects of the certification using Scala as programming language.

  • Scala Fundamentals
  • Core Spark - Transformations and Actions
  • Spark SQL and Data Frames
  • File formats
  • Flume, Kafka and Spark Streaming
  • Apache Sqoop

CCA-175: CCA Spark and Hadoop Developer Exam is a remote-proctored exam available anywhere, anytime. See below details for more information and system requirements. CCA-175: CCA Spark and Hadoop Developer Exam is a hands-on, practical exam using Cloudera technologies. Each user is given their own CDH6 (currently 6.1.1) cluster pre-loaded with Spark 2.4.

This exam is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

This exam requires the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants should learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers should also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The exam covers how to work with “big data” stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this exam, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.

Certification Path

CCA-175: CCA Spark and Hadoop Developer Exam is a fundamental exam. Successful completion by candidates will allow them to achieve Cloudera CCA 175 Spark Developer status.

Understanding functional and technical aspects of Scala Fundamentals

Scala is a great choice to learn and practice both Object-Oriented and Functional programming styles, because it offers a nice blend of both paradigms. To prepare Scala Fundamentals for this exam, you should evolve your understanding of Scala by learning about the new ideas and concepts it offers. You can start by learning how to write object-oriented code in Scala. Next, you can dive into the basic types that Scala provides and how you can create your own. You should know how to examine functions, including function literals, function values, and closures, before exploring how you can create your own Control Structures using the concept of currying. Finally, learn about Traits and how to create stackable modifications, how to use the reduce and fold concepts of Collections, and about Implicit conversion and all the use cases that the feature has to offer. By the end of preparation, you should be able to put these techniques into practice in your own Scala applications.

The questions in the exam will be about:

  • The basics of Scala.
  • The basics of type inference, variables, loops, functions, and operators.
  • How to use the REPL (the Scala Interactive Shell) for experimentation and fun
  • Understand the basics of Scala syntax, including val, var, def, if, while, try, for and more
  • Create classes, objects and instances
  • Define and use function literals and higher order functions
  • Understand the basics of Scala’s type inference and how to use it
  • Write custom control structures to augment those built into the language
  • Start down the path of a more functional style of programming
  • Use Scala’s extensive type system to write type-safe, easy to understand, abstract and concise code.
  • Ways to write constructs like classes, objects, case classes, and traits.
  • Concepts of type bounds and variance
  • How to read files using a console, perform pattern matching, and handle exceptions.
  • How to use classes, fields, methods, and objects.
  • Discover the elements of the functional programming style.
  • Apply the elements usefully in your daily programming tasks.
  • Develop a solid foundation for reasoning about functional programs, by touching upon proofs of invariants and the tracing of execution symbolically.

Understanding functional and technical aspects of Core Spark - Transformations and Actions

The following topics will be discussed in the exam

  • Setup Spark on Windows
  • Quick overview about Spark documentation
  • Initializing Spark job using spark-shell
  • Create Resilient Distributed Data Sets (RDD)
  • Previewing data from RDD
  • Reading different file formats - Brief overview using JSON
  • Transformations Overview
  • Manipulating Strings as part of transformations using Scala
  • Row level transformations using map
  • Row level transformations using flatMap
  • Filtering the data
  • Joining data sets - inner join
  • Joining data sets - outer join
  • Aggregations
  • Aggregations - using actions (reduce and countByKey)
  • Aggregations - understanding combiner
  • Aggregations using groupByKey - least preferred API for aggregations
  • Aggregations using reduceByKey
  • Aggregations using aggregateByKey
  • Sorting data using sortByKey
  • Global Ranking - using sortByKey with take and takeOrdered
  • By Key Ranking - Converting (K, V) pairs into (K, Iterable[V]) using groupByKey
  • Get topNPrices using Scala Collections API
  • Get topNPricedProducts using Scala Collections API
  • Get top n products by category using groupByKey, flatMap and Scala function
  • Set Operations - union, intersect, distinct as well as minus
  • Save data in Text Input Format
  • Save data in Text Input Format using Compression
  • Saving data in standard file formats - Overview
  • Revision of Problem Statement and Design the solution
  • Get Daily Revenue per Product - Launching Spark Shell
  • Get Daily Revenue per Product - Read and join orders and order_items
  • Get Daily Revenue per Product - Compute daily revenue per product id
  • Get Daily Revenue per Product - Read products data and create RDD
  • Get Daily Revenue per Product - Sort and save to HDFS
  • Add spark dependencies to sbt
  • Develop as Scala based application
  • Run locally using spark-submit
  • Ship and run it on big data cluster

Understanding functional and technical aspects of Spark SQL and Data Frames

  • Different interfaces to run Hive queries
  • Create Hive tables and load data in text file format
  • Create Hive tables and load data in ORC file format
  • Using spark-shell to run Hive queries or commands
  • Functions - Getting Started
  • Functions - Manipulating Strings
  • Functions - Manipulating Dates
  • Functions - Aggregations
  • Functions - CASE
  • Row level transformations
  • Create Joins
  • Create Aggregations
  • Create Sorting
  • Create Set Operations
  • Analytics Functions - Aggregations
  • Analytics Functions - Ranking
  • Windowing Functions
  • Create Data Frame and Register as Temp table
  • Writing Spark SQL Applications - process data
  • Writing Spark SQL Applications - Save data into Hive tables
  • Data Frame Operations
  • Use metastore tables as an input source or an output sink for Spark applications
  • Understand the fundamentals of querying datasets in Spark
  • Filter data using Spark
  • Write queries that calculate aggregate statistics
  • Join disparate datasets using Spark
  • Produce ranked or sorted data
  • Load data from HDFS for use in Spark applications
  • Write the results back into HDFS using Spark
  • Read and write files in a variety of file formats
  • Perform standard extract, transform, load (ETL) processes on data using the Spark API

Understanding functional and technical aspects of Flume, Kafka and Spark Streaming

Apache Spark is the single most revolutionizing phenomenon in Big Data Technologies. Spark turns infrastructure into a service, making provisioning hardware fast, simple, and reliable. Knowing this, many companies are transporting their big data analysis, staging, and storing needs to the Spark Framework.

The following will be discussed here:

  • Understanding Data Ingestion
  • Import data from a MySQL database into HDFS using Sqoop
  • Export data to a MySQL database from HDFS using Apache Sqoop
  • Change the delimiter and file format of data during import using Sqoop
  • Ingest real-time and near-real-time (NRT) streaming data into HDFS using Flume
  • Load data into and out of HDFS using the Hadoop File System (FS) commands
  • How to Transform Stage Store
  • Load data from HDFS and store results back to HDFS using Spark
  • Join disparate datasets together using Spark
  • Calculate aggregate statistics using Spark. Example: average or sum
  • Filter data into a smaller dataset using Spark
  • Write a query that produces ranked or sorted data using Spark
  • Read and create a table in the Hive meta-store in a given schema
  • Extract an Avro schema from a set of data files using Avro-tools
  • Create a table in the Hive meta-store using the Avro file format and an external schema file
  • Improve query performance by creating partitioned tables in the Hive meta-store
  • Evolve an Avro schema by changing JSON files

Who should take the CCA-175: CCA Spark and Hadoop Developer Exam

People looking to further their career in IT and Data Science via developing Spark Applications to manage Data Analytics.

Candidates should have the following knowledge/capabilities:

  • Cloudera Virtual Machine Installed
  • Apache Spark Programming experience
  • Describe valid uses/methods of Scala and Spark

How much CCA-175: CCA Spark and Hadoop Developer Exam

Examination Name: CCA-175: CCA Spark and Hadoop Developer Exam Passing Score: 70% or higher Length of Exam: 120 min Types of questions: Performance Based Questions No. of Questions: 8 to 12 Questions Examination Fees: $295 USD

How to book the CCA-175: CCA Spark and Hadoop Developer Exam

Register or log in on Cloudera website, or visit the Candidate Portal, or contact Cloudera Support for more information.

Average Salary of CCA-175: CCA Spark and Hadoop Developer Exam Certified Professionals

The average salary of a AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:

  • United State - 121,682 USD
  • India - 9,71,117 INR
  • Europe - 97,440 EURO
  • England - 61,267 POUND

The benefit of obtaining the CCA-175: CCA Spark and Hadoop Developer Exam Certification

If you work with F5 on a daily basis you are well aware there is more to becoming an F5 expert than memorizing commands and configuring BIG-IP modules. True application delivery administrators / engineers know you need skills ranging from networking, server administration, security, to development.

Difficulty in Attempting CCA-175: CCA Spark and Hadoop Developer Exam

If the user has successfully passed the CCA-175 practice exam and has been through CCA-175 dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.

For more info read reference:

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