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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. Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
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) 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.

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

3. 40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A) result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
B) result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()
C) result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
D) result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))

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

5. 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) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
B) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.
C) 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.
D) 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.

Solutions: