5 fully solved practice tests to help you prepare for the CCA Spark & Hadoop Developer certification & helps pass the CCA175 exam.

Students enrolling on this course can be 100% confident that after working on the test questions contained here they will be in a great position to pass the CCA175 exam.

As the number of vacancies for big data, machine learning & data science roles continue to grow, so too will the demand for qualified individuals to fill those roles.

It’s often the case the case that to stand out from the crowd, it’s necessary to get certified.

This exam preparation series has been designed to help YOU pass the Cloudera certification CCA175, this is a hands-on, practical exam where the primary focus is on using Apache Spark to solve Big Data problems.

On solving the questions contained here you’ll have all the necessary skills & the confidence to handle any questions that come your way in the exam.

(a) There are 5 practice tests contained in this course. All of the questions are directly related to the CCA175 exam syllabus.

(b) Fully worked out solutions to all the problems.

(c) Also included is the Verulam Blue virtual machine which is an environment that has a spark Hadoop cluster already installed so that you can practice working on the problems.

• The VM contains a Spark stack which allows you to read and write data to & from the Hadoop file system as well as to store metastore tables on the Hive metastore.

• All the datasets you need for the problems are already loaded onto HDFS, so you don’t have to do any extra work.

• The VM also has Apache Zeppelin installed with fully executed Zeppelin notebooks that contain solutions to all the questions.

Students will get hands-on experience working in a Spark Hadoop environment as they practice:

• Converting a set of data values in a given format stored in HDFS into new data values or a new data format and writing them into HDFS.

• Loading data from HDFS for use in Spark applications & writing the results back into HDFS using Spark.

• Reading and writing files in a variety of file formats.

• Performing standard extract, transform, load (ETL) processes on data using the Spark API.

• Using metastore tables as an input source or an output sink for Spark applications.

• Applying the understanding of the fundamentals of querying datasets in Spark.

• Filtering data using Spark.

• Writing queries that calculate aggregate statistics.

• Joining disparate datasets using Spark.

• Producing ranked or sorted data.