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

1. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Fetch the logs by running a Spark job with thespark-sqlCLI tool.
B) Locate the executor logs on the Spark master node, typically under the/tmpdirectory.
C) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
D) Use the commandspark-submitwith the-verboseflag to print the logs to the console.

2. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Query the users_vw using Spark
B) Recreate the users_vw and query the data using Spark
C) Save the users_vw definition and query using Spark
D) Persist the users_vw data as a table

3. The following code fragment results in an error:

Which code fragment should be used instead?

A)

B)

C)

D)

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

5. A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
C) Shuffle join because no broadcast hints were provided
D) Shuffle join, because AQE is not enabled, and Spark uses a static query plan

Solutions: