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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 observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

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

2. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.parquet("events.parquet").printSchema()
B) spark.sql("SELECT schema FROM events.parquet").show()
C) spark.sql("SELECT * FROM events.parquet").show()
D) spark.read.format("parquet").load("events.parquet").show()

3. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions to a fixed value, such as 200
B) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
C) Set the number of partitions equal to the total number of CPUs in the cluster
D) Set the number of partitions equal to the number of nodes in the cluster

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

5. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[False, True]).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.orderBy(col("age").asc(), col("salary").asc()).show()
D) df.sort("age", "salary", ascending=[True, True]).show()

Solutions: