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

1. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/*")
C) df = spark.read.parquet("/path/events/data/")
D) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")

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

3. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[False, True]).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.sort("age", "salary", ascending=[True, True]).show()
D) df.orderBy(col("age").asc(), col("salary").asc()).show()

4. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

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

5. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. 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)

Solutions: