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

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

What is the role of the driver node?

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

2. In the code block below, aggDF contains 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) complete
B) append
C) replace
D) aggregate

3. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
B) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
C) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
D) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.

4. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Reduce the value of spark.sql.shuffle.partitions
D) Increase the size of the dataset to create more partitions

5. 38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
B) CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
C) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');
D) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');

Solutions: