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

2. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) aggregate
B) append
C) complete
D) replace

3. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the number of executor instances to handle more concurrent tasks.
B) Reduce the size of the data partitions to improve task scheduling.
C) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
D) Increase the executor memory allocation in the Spark configuration.

4. A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
C) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
D) exploded_df = exploded_df.select("record_datetime", "record_exploded")

5. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

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

Solutions: