Databricks Certified Associate for Spark 3.0 Practice Exams

Databricks Certified Associate Developer for Apache Spark

The Databricks Certified Associate Developer for Apache Spark 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; and 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. Individuals who pass this certification exam can be expected to complete basic Spark DataFrame tasks using Python or Scala.

• Understanding the basics of the Spark architecture, including Adaptive Query Execution

• 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

There are 60 multiple-choice questions on the certification exam. The questions will be distributed by high-level topic in the following way:

• Apache Spark Architecture Concepts – 17% (10/60)

• Apache Spark Architecture Applications – 11% (7/60)

• Apache Spark DataFrame API Applications – 72% (43/60)

Total number of questions : 60

Time limit 120 minutes

Question types Multiple choice

Languages English