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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. An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A) The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
B) The code fails to execute because PySpark does not support joining DataFrames with a different structure
C) The code fails to execute because the column names employee_id and emp_id do not match automatically
D) The code fails to execute because it must use on='employee_id' to specify the join column explicitly

2. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'sep' ','
B) 'header' 'false'
C) 'header' 'true'
D) 'sep' '|'

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

4. 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 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.
C) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
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.

5. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) In-memory computation and parallel processing capabilities
B) Ability to process small datasets efficiently
C) Support for SQL queries on structured data
D) Built-in machine learning libraries

Solutions: