Cloudera’s CCA 175 — The 2020 Update: What Changed, What Didn’t, and How to Prepare for the Exam
By: Mahdi Karabiben
Tags: Apache Spark, Hadoop, Cloudera, Big Data, Certification

Cloudera’s CCA 175 — The 2020 Update: What Changed, What Didn’t, and How to Prepare for the Exam

The new version of Cloudera’s CCA-175 certification removes all of the legacy tools and puts the focus entirely on Apache Spark

Cloudera’s logo

A few weeks ago, Cloudera re-launched their Spark and Hadoop Developer Exam (CCA 175) with an updated exam environment and a set of key updates to the exam’s contents.

The exam continues to have a hands-on approach with a set of 8 to 12 performance-based tasks that the test-taker needs to perform on a Cloudera Quickstart virtual machine using the command-line, with access to most Big Data tools (Hive, Spark, and HDFS are all accessible via their corresponding commands).

If you’re planning on taking the exam in the upcoming weeks, below are the key elements to keep in mind and the most crucial tools and functionalities that you should focus on while preparing for it:

It’s all about Spark

For some reason, Cloudera kept giving at least two Sqoop-related tasks in the CCA 175 exam until the last months of 2019. Sqoop, although it was widely used at the start of the past decade, has been more of a legacy tool for a few years, and a very limited number of companies still expect candidates to be familiar with it.

Luckily, Cloudera took the cue and removed Sqoop-related tasks from the exam. Flume-related tasks (which weren’t as frequent) have also been removed, making the exam completely focused on the usage of Big Data’s most prominent tool, Apache Spark.

The key update of the 2020 version is that Spark 2.4 is now provided by default within the exam environment (instead of Spark 1.6), accessible via both Spark Shell (for Scala) and Pyspark (for Python), or even via Spark-submit if you prefer using scripts while performing the exam’s tasks. Spark 2.4 introduced a set of very useful features that make performing well at the exam a much easier task, notably when working with Avro files.

Get familiar with Hive and HDFS

Certain tasks within the exam still necessitate basic knowledge of Apache Hive and HDFS. Mainly, you need to be comfortable with the process of creating Hive tables with the different options related to the location of the data (if you’re asked to create an external table) and its format.

Also, at certain points, the examinee will need to use HDFS commands (mostly ls and tail ) to determine the format in which the input data is stored and to get an initial look at the data that they’ll work with.

The exam’s new version is definitely Spark-oriented, but not knowing basic Hive and HDFS commands can cost you multiple tasks and eventually prevent you from receiving the certificate.

Read, process, write

The exam’s tasks are actually different versions of the same problem. You have to read data from HDFS in a certain format (mostly text or parquet) using Spark, process it via filters, maps, aggregations, and joins, and then write it back to HDFS in a different format and using a certain compression codec.

Mastering this process will guarantee you a pleasant experience when taking the exam because you’ll simply be asked to do it multiple times on different datasets.

Time management is also an important factor, so make sure to start by reading all of the tasks at the beginning of the exam, and then start right away with the ones that you’re most comfortable with.