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

1. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.mode("overwrite").json("path/to/file")
B) df.write.mode("append").json("path/to/file")
C) df.write.json("path/to/file")
D) df.write.option("overwrite").json("path/to/file")

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

3. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger() - default micro-batch trigger without interval.

4. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the filter transformation is applied
B) When the count action is applied
C) When the groupBy transformation is applied
D) When the show action is applied

5. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
D) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

Solutions: