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

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

2. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write.option("path", "/some/path").saveAsTable("default_table")
B) users.write.saveAsTable("default_table", path="/some/path")
C) users.write(path="/some/path").saveAsTable("default_table")
D) users.write.saveAsTable("default_table").option("path", "/some/path")

3. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) The code throws an error due to a schema mismatch.
B) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
C) [Row(name='bambi'), Row(name='alladin', age=20)]
D) [Row(name='alladin', age=20)]

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

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

Solutions: