CCA175 Spark & Hadoop Developer Exam Practice Sets

Note: This course is personally designed from my own experience and has no affiliation with any person or organization. This practice course and its videos, documents and other associated content has been produced by myself. This is an unofficial content and has no relation with Cloudera or anyone else. Content in this practice sets are solely meant to help student and build confidence for the real Cloudera CCA175 Spark & Hadoop Developer Exam.

Course Descriptions:

This course is designed for Cloudera CCA175 Spark & Hadoop Developer Exam practice. To practice this sets all you need is Cloudera Quick Start VM with Spark 2.4 or higher installed. This practice course has scenario based problems, the way you might expect to see in real exam. Each set has time clock. At the beginning of each sets you're given instructions how to prepare the input data for all the problems that you'll be solving. You're given expected sample output, wherever possible. All problems are explained with step by step solutions and guided you how you should validate the results to make sure you answered the problem covering the output requirements. Each practice sets are thoughtfully designed to cover what you might encounter in real exam. This course will surely boost your confidence and help you appear for the exam without fear. In each practice sets I've covered all below topics of the latest CCA175 exam curriculum.

Transform, Stage, and Store

Convert a set of data values in a given format stored in HDFS into new data values or a new data format and write them into HDFS.

• 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

Data Analysis

Use Spark SQL to interact with the metastore programmatically. Generate reports by using queries against loaded data.

• 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