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

1. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.filter (col("count") < 30).show()
B) streaming_df.orderBy("timestamp").limit(4)
C) streaming_df.groupby("Id") .count ()
D) streaming_df. select (countDistinct ("Name") )

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

3. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 80
B) 160
C) 64
D) 40

4. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
D) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.

5. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
B) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")

Solutions: