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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. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").distinct().orderBy(df["Name"].desc())
B) df.select("Name").distinct()
C) df.select("Name").distinct().orderBy(df["Name"])
D) df.select("Name").orderBy(df["Name"].asc())

2. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold
B) Shuffle join because no broadcast hints were provided
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
D) Shuffle join, because AQE is not enabled, and Spark uses a static query plan

3. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
B) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
C) regions_dict = regions.select("region_id", "region_name").take(3)
D) regions_dict = dict(regions.take(3))

4. 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) 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.
B) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C) 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.
D) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.

5. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) APPEND
B) AGGREGATE
C) COMPLETE
D) REPLACE

Solutions: