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Databricks Certified Associate Developer for Apache Spark 3.0 Practice Exam

Databricks Certified Associate Developer for Apache Spark 3.0 


About Databricks Certified Associate Developer for Apache Spark 3.0 Exam

The Databricks Certified Associate Developer for Apache Spark 3.0 certification exam assesses the understanding of the Spark DataFrame API and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. These tasks include -

  • selecting, renaming and manipulating columns;
  • filtering, dropping, sorting, and aggregating rows;
  • handling missing data;
  • combining, reading, writing and partitioning DataFrames with schemas;
  • working with UDFs and Spark SQL functions. 

In addition, the exam will assess the basics of the Spark architecture like execution/deployment modes, the execution hierarchy, fault tolerance, garbage collection, and broadcasting.


Exam Prerequisites

The minimally qualified candidate should:

  • have a basic understanding of the Spark architecture, including Adaptive Query Execution
  • be able to apply the Spark DataFrame API to complete individual data manipulation task, including:
  • selecting, renaming and manipulating columns
  • filtering, dropping, sorting, and aggregating rows
  • joining, reading, writing and partitioning DataFrames
  • working with UDFs and Spark SQL functions


Who should take the exam?

  • It is expected that developers should use the Spark DataFrame API for six months or more should be able to pass this certification exam.
  • While it will not be explicitly tested, the candidate must have a working knowledge of either Python or Scala. The exam is available in both languages.


What you will learn?

  • Architecture of an Apache Spark Application
  • Learn to run Apache Spark on a cluster of computer
  • Learn the Execution Hierarchy of Apache Spark
  • Create DataFrame from files and Scala Collections
  • Spark DataFrame API and SQL functions
  • Different techniques to select the columns of a DataFrame
  • Define the schema of a DataFrame and set the data types of the columns
  • Apply various methods to manipulate the columns of a DataFrame
  • Filter your DataFrame based on specifics rules
  • Sort data in a specific order
  • Sort rows of a DataFrame in a specific order
  • Arrange the rows of DataFrame as groups
  • Handle NULL Values in a DataFrame
  • Use JOIN or UNION to combine two data sets
  • Save the result of complex data transformations to an external storage system
  • Different deployment modes of an Apache Spark Application
  • Working with UDFs and Spark SQL functions
  • Use Databricks Community Edition to write Apache Spark Code


Exam Details

The exam details are as follows:

  • Total Questions: 60
  • Exam Format: Multiple-choice questions.
  • Exam Duration: 120 minutes
  • Passing score: 70% and above (42 of the 60 questions)
  • Exam Type: Online proctored Exam


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  • Practice exam questions have been created on the basis of content outlined in the official documentation.
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