Gain a deep understanding of DataFrame repartitioning in Apache Spark, a key concept for the Databricks Certified Associate Developer for Apache Spark certification exam. Learn how to effectively manage DataFrame partitions to optimize your data processing tasks.
Table of Contents
Question
The code block shown below should return a new 12-partition DataFrame from DataFrame storesDF. Choose the response that correctly fills in the numbered blanks within the code block to complete this task.
Code block:
__1__.__2__(__3__)
A. 1. storesDF
2. coalesce
3. 4
B. 1. storesDF
2. coalesce
3. 4, “storeId”
C. 1. storesDF
2. repartition
3. “storeId”
D. 1. storesDF
2. repartition
3. 12
E. 1. storesDF
2. repartition
3. Nothing
Answer
D. 1. storesDF
2. repartition
3. 12
Explanation
The correct answer is D. The repartition method in Apache Spark is used to increase or decrease the partitions in a DataFrame. The number passed as an argument to the repartition method is the number of partitions desired. So, storesDF.repartition(12) will return a new DataFrame with 12 partitions from the DataFrame storesDF.
Databricks Certified Associate Developer for Apache Spark certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Databricks Certified Associate Developer for Apache Spark exam and earn Databricks Certified Associate Developer for Apache Spark certification.