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

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

1. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
B) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)

2. 46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
B) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
C) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.
D) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

3. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.sort("Name", "count")
B) df1.orderBy(col("count").desc(), col("Name").asc())
C) df1.orderBy(col("Name").desc(), col("count").asc())
D) df1.orderBy("Name", "count")

4. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Fetch the logs by running a Spark job with the spark-sql CLI tool.
B) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
C) Use the command spark-submit with the -verbose flag to print the logs to the console.
D) Locate the executor logs on the Spark master node, typically under the /tmp directory.

5. 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) 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.

Solutions: