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

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

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) 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) 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.

3. 37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF = customerDF.select("email").alias("username", "domain")
C) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
D) customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))

4. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It appends new partitions to an existing Parquet file.
B) It stores all data in a single Parquet file.
C) It creates separate directories for each unique combination of color and taste.
D) It throws an error if there are null values in either partition column.

5. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A broadcast nested loop join
B) A Cartesian join
C) A sort-merge join
D) A shuffled hash join

Solutions: