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Databricks Certified Associate Developer for Apache Spark 3.0: Certified Associate Developer for Apache Spark
Free Practice Exam Questions (page: 8)
Updated On: 2-Jan-2026

Page 8 of 46
PDF with 342 Questions

Which of the following code blocks reads JSON file imports.json into a DataFrame?

  1. spark.read().mode("json").path("/FileStore/imports.json")
  2. spark.read.format("json").path("/FileStore/imports.json")
  3. spark.read("json", "/FileStore/imports.json")
  4. spark.read.json("/FileStore/imports.json")
  5. spark.read().json("/FileStore/imports.json")

Answer(s): D

Explanation:

Static notebook | Dynamic notebook: See test 1, Question: 25 (
Databricks import instructions) (https://flrs.github.io/spark_practice_tests_code/#1/25.html ,
https://bit.ly/sparkpracticeexams_import_instructions)



Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?

  1. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))
  2. transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))
  3. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))
  4. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))
  5. transactionsDf.withColumn("predErrorSquared", "predError"**2)

Answer(s): C

Explanation:

While only one of these code blocks works, the DataFrame API is pretty flexible when it comes to accepting columns into the pow() method. The following code blocks would also work: transactionsDf.withColumn("predErrorSquared", pow("predError", 2)) transactionsDf.withColumn("predErrorSquared", pow("predError", lit(2)))
Static notebook | Dynamic notebook: See test 1, Question: 26 (
Databricks import instructions) (https://flrs.github.io/spark_practice_tests_code/#1/26.html ,
https://bit.ly/sparkpracticeexams_import_instructions)



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")

  1. The argument to the where method should be "predError >= 5".
  2. Instead of where(), filter() should be used.
  3. The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").
  4. The argument to the where method cannot be a string.
  5. Instead of >=, the SQL operator GEQ should be used.

Answer(s): A

Explanation:

The argument to the where method cannot be a string. It can be a string, no problem here.
Instead of where(), filter() should be used.
No, that does not matter. In PySpark, where() and filter() are equivalent. Instead of >=, the SQL operator GEQ should be used.
Incorrect.
The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").
No, Spark returns a new DataFrame.
Static notebook | Dynamic notebook: See test 1, Question: 27 (
Databricks import instructions) (https://flrs.github.io/spark_practice_tests_code/#1/27.html ,
https://bit.ly/sparkpracticeexams_import_instructions)



Which of the following code blocks saves DataFrame transactionsDf in location
/FileStore/transactions.csv as a CSV file and throws an error if a file already exists in the location?

  1. transactionsDf.write.save("/FileStore/transactions.csv")
  2. transactionsDf.write.format("csv").mode("error").path("/FileStore/transactions.csv")
  3. transactionsDf.write.format("csv").mode("ignore").path("/FileStore/transactions.csv")
  4. transactionsDf.write("csv").mode("error").save("/FileStore/transactions.csv")
  5. transactionsDf.write.format("csv").mode("error").save("/FileStore/transactions.csv")

Answer(s): E

Explanation:

Static notebook | Dynamic notebook: See test 1, question 28 (
Databricks import instructions) (https://flrs.github.io/spark_practice_tests_code/#1/28.html ,
https://bit.ly/sparkpracticeexams_import_instructions)



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