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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. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A job contains multiple stages, and each stage contains multiple tasks.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A stage contains multiple jobs, and each job contains multiple tasks.
D) A stage contains multiple tasks, and each task contains multiple jobs.

2. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.sort("Name", "count")
B) df1.orderBy(col("Name").desc(), col("count").asc())
C) df1.orderBy(col("count").desc(), col("Name").asc())
D) df1.orderBy("Name", "count")

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

A) 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
B) 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.
C) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
D) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.

4. A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?

A) def shake_256(df: pd.Series) -> str:
B) def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:
C) def shake_256(df: pd.Series) -> pd.Series:
D) def shake_256(raw: str) -> str:

5. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

A) It is not able to handle deduplication in this scenario.
B) It removes all duplicates regardless of when they arrive.
C) It removes duplicates that arrive within the 30-minute window specified by the watermark.
D) It accepts watermarks in seconds and the code results in an error.

Solutions: