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

1. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A stage contains multiple jobs, and each job contains multiple tasks.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A stage contains multiple tasks, and each task contains multiple jobs.
D) A job contains multiple stages, and each stage contains multiple tasks.

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

3. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Improving the performance of single-stage Spark jobs
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Dynamically switching join strategies
D) Optimizing the layout of Delta files on disk

4. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=False)
B) df = au_df.unionAll(nz_df)
C) df = au_df.unionByName(nz_df, allowMissingColumns=True)
D) df = au_df.union(nz_df, allowMissingColumns=True)

5. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
D) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

Solutions: