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

1. 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.

2. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) Data will be lost during conversion
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows

3. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It runs on a single node only, utilizing memory efficiently.
B) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
C) It computes results immediately using eager execution.
D) It is available only with Python, thereby reducing the learning curve.

4. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.to_date("timestamp")).show()
B) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
C) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
D) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()

5. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A job contains multiple tasks, and each task contains multiple stages.
B) A stage contains multiple tasks, and each task contains multiple jobs.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A stage contains multiple jobs, and each job contains multiple tasks.

Solutions: