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

1. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be added to the start date.
B) The same start date will be returned.
C) An error message of an invalid parameter will be returned.
D) The number of days specified will be removed from the start date.

2. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
C) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
D) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

3. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to local mode.
B) Increase the driver memory on the client machine.
C) Add more executor instances to the cluster.
D) Switch the deployment mode to cluster mode.

4. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
B) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

5. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
B) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
C) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
D) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy

Solutions: