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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. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It creates separate directories for each unique combination of color and taste.
B) It stores all data in a single Parquet file.
C) It throws an error if there are null values in either partition column.
D) It appends new partitions to an existing Parquet file.

2. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.select(countDistinct("name"))
B) streaming_df.show()
C) streaming_df.filter("count < 30")
D) streaming_df.count()

3. What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame
C) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
D) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.

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

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

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

Solutions: