Databricks Databricks Certification Associate-Developer-Apache-Spark-3.5

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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:

Question #1
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. 20
B. Same number as the cluster executors
C. 10
D. 1

Question #2
2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.option("overwrite").json("path/to/file")
B. df.write.mode("append").json("path/to/file")
C. df.write.json("path/to/file")
D. df.write.mode("overwrite").json("path/to/file")

Question #3
33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
C. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
D. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))

Question #4
28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A. coalesce
B. filter
C. select
D. groupBy

Question #5
48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
B. The code will fail because only one broadcast join can be performed at a time.
C. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
D. The code will result in an error because broadcast() must be called before the joins, not inline.

Solutions: