Databricks Associate-Developer-Apache-Spark-3.5 Exam : Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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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. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

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

2. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) save with mode Ignore
B) saveAsTable with mode Overwrite
C) saveAsTable with mode ErrorIfExists
D) save with mode ErrorIfExists

3. 26 of 55.
A data scientist at 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 scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("name", "email", "birthdate")
C) df_user_non_pii = df_user.select("name", "email", "birthdate")
D) df_user_non_pii = df_user.remove("name", "email", "birthdate")

4. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
B) Reduce the size of the data partitions to improve task scheduling.
C) Increase the number of executor instances to handle more concurrent tasks.
D) Increase the executor memory allocation in the Spark configuration.

5. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) REPLACE
B) AGGREGATE
C) APPEND
D) COMPLETE

Solutions: