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

1. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

2. 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) 64
B) 80
C) 40
D) 160

3. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_spark()
B) psdf.to_pyspark()
C) psdf.to_pandas()
D) psdf.to_dataframe()

4. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

A)

B)

C)

D)

5. In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) replace
B) append
C) complete
D) aggregate

Solutions: