How To Pass Databricks Certified Associate Developer for Apache Spark 3.0 Exam Easily?

How To Pass Databricks Certified Associate Developer for Apache Spark 3.0 Exam Easily?

Databricks Certified Associate Developer for Apache Spark 3.0 certification exam assesses an understanding of the basics of the Spark architecture and the ability to apply the Spark DataFrame API to complete individual data manipulation tasks. FreeTestShare provides 100% real and reliable Databricks Certified Associate Developer for Apache Spark 3.0 questions and answers files to help you pass in the first try.

This Databricks Certified Associate Developer for Apache Spark 3.0 free demo aims to make you well-prepared. Test now! 

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1. Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

2. The code block displayed below contains an error. The code block is intended to return all columns of DataFrame transactionsDf except for columns predError, productId, and value. Find the error.

Excerpt of DataFrame transactionsDf:

transactionsDf.select(~col("predError"), ~col("productId"), ~col("value"))

3. Which of the following code blocks sorts DataFrame transactionsDf both by column storeId in ascending and by column productId in descending order, in this priority?

4. The code block displayed below contains an error. The code block should return a new DataFrame that only contains rows from DataFrame transactionsDf in which the value in column predError is at least 5. Find the error.

Code block:

transactionsDf.where("col(predError) >= 5")

5. Which of the following statements about RDDs is incorrect?

6. .sum(col('value'))

7. Which of the following code blocks generally causes a great amount of network traffic?

8. Which of the following statements about garbage collection in Spark is incorrect?

9. +-------------+---------+-----+-------+---------+----+

10. +-------+-------------+---------+-----+

Code block:

sc.union([transactionsDfMonday, transactionsDfTuesday])


 

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