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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) .option("format", "parquet")
.option("location", "path/to/destination/dir")
B) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
C) .format("parquet")
.option("location", "path/to/destination/dir")
D) .format("parquet")
.option("path", "path/to/destination/dir")

2. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) 10
B) 20
C) Same number as the cluster executors
D) 1

3. 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.IntegerType())
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.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

4. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
C) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
D) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.

5. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
B) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
C) spark.conf.set("spark.pandas.arrow.enabled", "true")
D) spark.conf.set("spark.sql.execution.arrow.enabled", "true")

Solutions: