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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 5
B) Same number as the cluster executors
C) 10
D) 20

2. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A Cartesian join
B) A broadcast nested loop join
C) A sort-merge join
D) A shuffled hash join

3. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
B) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
C) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
D) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges

4. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be added to the start date.
B) The number of days specified will be removed from the start date.
C) The same start date will be returned.
D) An error message of an invalid parameter will be returned.

5. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
B) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
C) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
D) spark.read.parquet("/data/parquet/")

Solutions: