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.
Databricks Developer for Apache Spark – Python Exam Summary:
Databricks Apache Spark Developer Associate Exam Syllabus Topics:
| Topic |
Weights |
| Apache Spark Architecture Concepts |
17% |
| Apache Spark Architecture Applications |
11% |
| Apache Spark DataFrame API Applications |
72% |
Databricks Cert 