Databricks Certified Associate Developer for Apache Spark Practice Exam

The Databricks Certified Associate Developer for Apache Spark exam validates your foundational skills in using Apache Spark for data manipulation tasks within the Databricks platform. Earning this certification demonstrates your ability to leverage Spark DataFrames and the Spark SQL API to process and analyze big data efficiently.

Who Should Take This Exam?

This Databricks certification is ideal for IT professionals, including:

• Software Developers: Seeking to expand their skillset into big data processing using Apache Spark on Databricks.
• Data Analysts: Aspiring to leverage Spark for data manipulation and analysis tasks within their workflow.
• Data Engineers (Spark Focus): Demonstrating their proficiency in using Spark on Databricks for data wrangling and ETL (Extract, Transform, Load) processes.
• Anyone New to Big Data Processing: Gaining a foundational understanding of Apache Spark for data manipulation on a leading cloud platform.

Prerequisites

There are no formal prerequisites for taking the exam. However, a basic understanding of programming concepts and some familiarity with data analysis principles would be beneficial.

Roles and Responsibilities

• Spark Developer (Databricks Focus): Developing Spark applications for data manipulation and analysis on the Databricks platform.
• Data Analyst (Spark Skills): Utilizing Spark for data cleaning, transformation, and analysis tasks in support of data-driven decision making.
• Big Data Engineer (Entry Level): Contributing to big data processing pipelines by manipulating data with Spark on Databricks.
• Data Science Enthusiast (Spark Foundation): Demonstrating a foundational understanding of Spark for potential data science or big data career paths.

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 Objectives

• 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