CCA 175 Spark and Hadoop Developer Certification Exam Guide

Eligibility and Prerequisites

• Basic Programming Knowledge – Proficiency in Python or Scala is essential.
• Understanding of Big Data Concepts – Familiarity with Hadoop, Spark, and distributed computing.
• Experience with Linux/Unix – Ability to work in a command-line environment.
• Knowledge of HDFS – Understanding Hadoop Distributed File System operations.
• SQL Skills – Ability to write queries and work with Hive tables.
• Data Formats – Familiarity with JSON, Avro, Parquet, etc.
• Hands-on Practice – Experience running Spark jobs in a real or simulated cluster.
• No Formal Degree Required – Open to all backgrounds with relevant skills.




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Spark and Hadoop Topics

The core of the CCA 175 exam lies in assessing practical application. Key areas include Spark Core and Spark SQL, where candidates write optimized applications using RDDs and DataFrames. Another important area is data ingestion, where tools like Sqoop and Flume are used to import data from relational databases and real-time sources into HDFS. Other covered topics include data transformation workflows using Spark, writing data in different formats, querying large datasets with Hive and Impala, and understanding HDFS file systems. Additionally, candidates need to be comfortable with compression techniques, partitioning strategies, and data serialization formats. The CCA 175 exam focuses on key Spark and Hadoop topics essential Big Data Training for big data development. Core Spark topics include Spark Core APIs for RDD transformations and actions, Spark SQL for querying structured data, and Spark Streaming for real-time data processing. Candidates must understand working with DataFrames, datasets, and Spark configurations.



Hadoop-related topics cover HDFS architecture, file operations, and data ingestion using tools like Sqoop and Flume. The exam also includes Hive integration, data serialization formats (Avro, Parquet, JSON), and performance tuning techniques. Proficiency in using Scala or Python to write Spark applications is critical for passing the certification.





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Hands-On Lab Setup

• System Requirements – Minimum 8GB RAM, 64-bit OS, and sufficient disk space.
• Install VirtualBox/VMware – Set up a virtual environment for Cloudera QuickStart VM.
• Download Cloudera QuickStart VM – Get the official VM image for practice.
• Install Hadoop and Spark – Alternatively, install components manually using a Linux distribution.
• Set Up IDE – Use IntelliJ IDEA or VS Code for Scala/Python development.
• Configure Environment Variables – Set paths for Hadoop, Spark, and Java.
• Test Sample Jobs – Run basic Spark and Hadoop commands.
• Practice Exam Tasks – Simulate real-world tasks in your setup.






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Tips for Preparing

Tips for Preparing for the CCA 175 exam involves both theoretical study and practical execution. Begin by thoroughly reviewing Cloudera’s official exam guide and syllabus. Use practice books like “Learning Spark” and “Hadoop: The Definitive Guide” to strengthen foundational concepts. Online courses on Udemy, Coursera, and DataCamp provide structured content and hands-on labs. Solve sample problems and take mock exams to get accustomed to the exam pattern. Most importantly, remember that the exam is open-terminal with no internet access, so practice syntax and commands rigorously. Preparing for the CCA 175 exam requires a strategic approach. Start by mastering Spark and Hadoop fundamentals through official documentation and online courses. Focus on hands-on practice using the Cloudera QuickStart VM or a custom lab setup. Practice writing Spark applications in Python or Scala, and work extensively with HDFS, Hive, and various data formats like JSON and Parquet. Simulate real exam tasks and time yourself to build speed and accuracy. Join forums or study groups for peer support and insights. Review past exam experiences and practice mock scenarios. Consistent coding and real-world data problem solving are key to success.




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Sample Questions and Answers

To help candidates understand the format and expectations, here are a few sample tasks:

Ingesting Data from MySQL using Sqoop:

• sqoop import –connect jdbc:mysql://localhost/db –username user –password pass \ –table employees –target-dir /user/hadoop/employees

Writing a Basic Spark Transformation in Python:

• rdd = sc.textFile(“hdfs://input.txt”)
• words = rdd.flatMap(lambda x: x.split(” “))
• wordCount = words.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y)
• wordCount.saveAsTextFile(“hdfs://output”)

Creating and Loading Data into a Hive Table:

• CREATE TABLE emp (id INT, name STRING, salary FLOAT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’;
• LOAD DATA INPATH ‘/user/hive/emp.csv’ INTO TABLE emp;





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Conclusion

The CCA 175 certification is a valuable credential for aspiring big data developers, validating their practical skills in Apache Spark and Hadoop ecosystems. It emphasizes real-world data processing tasks, requiring candidates to demonstrate their ability to ingest, transform, and analyze data using Spark Core, Spark SQL, Recommended Study Resources and Spark Streaming. With its hands-on, performance-based format, the exam ensures that certified individuals are job-ready and capable of working in production environments Big Data Training. Preparing for CCA 175 involves a solid understanding of big data concepts, programming proficiency in Python or Scala, and hands-on experience with tools like HDFS, Hive, and Sqoop. Setting up a local lab or using virtual environments like the Cloudera QuickStart VM is crucial for gaining practical experience. Candidates should also focus on working with different data formats and optimizing performance in distributed systems. Successfully earning the CCA 175 certification not only boosts your resume but also opens doors to roles such as data engineer, big data developer, or Spark specialist. With the growing demand for data-driven solutions across industries, certified professionals are well-positioned to take advantage of numerous career opportunities. Consistent practice, a clear study plan, Sample Questions and