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

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

2. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) employees_df.filter(employees_df.tenure >= 5).show()
B) employees_df.where(employees_df.tenure >= 5)
C) employees_df.filter(employees_df.tenure >= 5).collect()
D) filter(employees_df.tenure >= 5)

3. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option checkpointLocation during readStream
B) By configuring the option recoveryLocation during writeStream
C) By configuring the option recoveryLocation during the SparkSession initialization
D) By configuring the option checkpointLocation during writeStream

4. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
B) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
C) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
D) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159

5. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_spark()
B) pdf.to_dataframe()
C) pdf.to_pandas()
D) pdf.spark()

Solutions: