In this post we will see the preparation strategy for Databricks Certified Associate Developer for Apache Spark Exam.
This certification exam assesses the understanding of the Spark DataFrame/SQL API and the ability to apply the Spark DataFrame/SQL API to complete basic data manipulation tasks within a Spark session.
Now we will see the Topics covered in this exam which we need to prepare before appearing for this exam.
Spark Architecture :
- Spark driver concept
- Spark job, stages, tasks, slots term.
- spark configuration properties
- Spark mode of execution concept
Spark DataFrame/SQL API :
- Creation of the Dataframes
- Chaining concept
- Selecting the single/multiple columns
- Renaming the columns
- conditional transformation like use of when-otherwise methods.
- Filtering the records
- Dropping the single/multiple columns
- Dropping the duplicates records
- Sorting records based on single column/ multiple columns
- Aggregating functions like sum, max, min etc
- Joining multiple dataframes/tables
- Reading input files like parquet, csv etc
- Writing dataframe into files like csv, parquet etc
- User Defined Function (UDF) creation and usage
- Different Storage levels of dataframe/tables
- Cache() , persist() and checkpointing() usage
- Shared variables like broadcast , accumulator concept
- Coalesce and Repartition concepts
Most Important point: Please go through sample paper (Python : Sample Paper | Scala : Sample Paper ) before giving the actual exam. This sample paper will help you to understand the paper pattern and revising your spark concepts. The actual paper will be mostly similar to the sample paper only.
Exam Duration:
2 hours (120 minutes) will be given to complete the exam.
Passing Criteria:
If you are able to give correct answers for 42 out of 60 questions (70%) then you passed the exam.
Reference: https://www.databricks.com/learn/certification/apache-spark-developer-associate
You can utilize below study materials for your preperation :
Thank you for reading this post.