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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

16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A. Transformations are evaluated lazily.
B. Transformations are executed immediately to build the lineage graph.
C. Only actions trigger the execution of the transformation pipeline.
D. The Spark engine optimizes the execution plan during the transformations, causing delays.
E. The Spark engine requires manual intervention to start executing transformations.

Question 2

A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A. .trigger(availableNow=True)
B. .trigger(continuous=True)
C. .trigger(continuous='1 second')
D. .trigger(processingTime='1 second')

Question 3

An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It increases the partition size for df1 and df2.
B. It ensures that the join happens only when the id values are identical.
C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
D. It filters the id values before performing the join.

Question 4

39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A. Use an accumulator to record the maximum time on the driver.
B. Broadcast a variable to share the maximum time among workers.
C. Use an RDD action like reduce() to compute the maximum time.
D. Configure the Spark UI to automatically collect maximum times.

Question 5

A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A. Modify the Spark configuration to disable garbage collection
B. Cache large DataFrames to persist them in memory.
C. Increase the memory allocated to the Spark Driver.
D. Optimize the data processing logic by repartitioning the DataFrame.

Solutions: