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

1. 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.

2. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
B) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
D) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

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

4. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Persist the users_vw data as a table
B) Save the users_vw definition and query using Spark
C) Recreate the users_vw and query the data using Spark
D) Query the users_vw using Spark

5. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.format("parquet").load("events.parquet").show()
B) spark.sql("SELECT * FROM events.parquet").show()
C) spark.sql("SELECT schema FROM events.parquet").show()
D) spark.read.parquet("events.parquet").printSchema()

Solutions: