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

1. A developer runs:

What is the result?
Options:

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

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

3. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B) Set the number of partitions to a fixed value, such as 200
C) Set the number of partitions equal to the number of nodes in the cluster
D) Set the number of partitions equal to the total number of CPUs in the cluster

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

5. 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.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
B) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
C) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
D) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)

Solutions: