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

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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. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources
B) It can be used to interact with any remote cluster using the REST API
C) It provides a way to run Spark applications remotely in any programming language
D) It allows for remote execution of Spark jobs

3. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
C) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.start()

4. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_spark()
B) psdf.to_dataframe()
C) psdf.to_pandas()
D) psdf.to_pyspark()

5. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
B) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
D) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

Solutions: