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

1. 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) 160
B) 40
C) 64
D) 80

2. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(continuous='1 second')
B) .trigger(availableNow=True)
C) .trigger(continuous=True)
D) .trigger(processingTime='1 second')

3. Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A) dropDuplicates with no arguments (removes based on all columns)
B) dropDuplicates on all columns (wrong criteria)
C) groupBy without aggregation (invalid use)
D) dropDuplicates on the exact matching fields

4. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) A new SparkSession is created every time the getOrCreate() method is invoked.
B) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
C) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
D) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.

5. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
B) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
D) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

Solutions: