Hadoop Practice Exam

Hadoop Practice Exam

4.7 (139 ratings)
161 Learners

What’s Included

No. of Questions 105
Access Immediate
Access Duration Life Long Access
Exam Delivery Online
Test Modes Practice, Exam

Hadoop Practice Exam

The Certificate in Hadoop provides Candidates with a comprehensive understanding of the Hadoop ecosystem, including Hadoop Distributed File System (HDFS), MapReduce, and related technologies. Candidates learn how to store, process, and analyze large volumes of data using Hadoop. The course covers key topics such as Hadoop architecture, HDFS fundamentals, MapReduce programming, and data processing with Hive and Pig.

The certification covers skills in Hadoop architecture, HDFS, MapReduce programming, Hive, Pig, and basic data analysis.

Candidates should have a basic understanding of programming concepts and experience with Linux operating system. Familiarity with Java programming language is beneficial.
Why is Hadoop important?

  • Handles large volumes of data efficiently
  • Enables distributed computing for faster data processing
  • Used in big data analytics to derive insights from data
  • Scalable and cost-effective solution for storing and processing big data

Who should take the Hadoop Exam?

  • Big Data Engineer
  • Data Analyst
  • Hadoop Developer
  • Data Scientist
  • Database Administrator

Skills Evaluated

Candidates taking the certification exam on the Hadoop is evaluated for the following skills:

  • Hadoop Architecture
  • HDFS Fundamentals
  • MapReduce Programming
  • Hive and Pig
  • Data Processing and Analysis

Hadoop Certification Course Outline

  • Introduction to Hadoop
    • Overview of Big Data
    • Hadoop Ecosystem
  • Hadoop Architecture
    • HDFS Architecture
    • MapReduce Architecture
  • HDFS Fundamentals
    • Data Storage in HDFS
    • Data Replication and Fault Tolerance
  • MapReduce Programming
    • MapReduce Concepts
    • Developing MapReduce Applications
  • Hive and Pig
    • Introduction to Hive
    • Introduction to Pig
  • Data Processing and Analysis
    • Data Ingestion
    • Data Transformation
    • Data Analysis with Hadoop

 

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