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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. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Stop and restart the SparkSession after each action.
B) Avoid using a SparkSession and rely on SparkContext only.
C) Create a new SparkSession instance before each transformation.
D) Use a single SparkSession instance for the entire application.

2. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Only actions trigger the execution of the transformation pipeline.
B) The Spark engine requires manual intervention to start executing transformations.
C) The Spark engine optimizes the execution plan during the transformations, causing delays.
D) Transformations are evaluated lazily.
E) Transformations are executed immediately to build the lineage graph.

3. 37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF = customerDF.select("email").alias("username", "domain")
C) customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
D) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])

4. 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) COMPLETE
D) APPEND

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

Solutions: