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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Transformations are executed immediately to build the lineage graph.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) Transformations are evaluated lazily.
D) The Spark engine requires manual intervention to start executing transformations.
E) Only actions trigger the execution of the transformation pipeline.

2. What is the difference between df.cache() and df.persist() in Spark DataFrame?

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

3. Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

A) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
B) The driver node only provides the user interface for monitoring the application
C) The driver node stores the final result after computations are completed by worker nodes
D) The driver node holds the DataFrame data and performs all computations locally

4. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B) Uses trigger(continuous='5 seconds') - continuous processing mode.
C) Uses trigger() - default micro-batch trigger without interval.
D) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

5. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
B) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
C) regions_dict = regions.select("region_id", "region_name").take(3)
D) regions_dict = dict(regions.take(3))

Solutions: