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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. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.

2. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.parquet("events.parquet").printSchema()
B) spark.read.format("parquet").load("events.parquet").show()
C) spark.sql("SELECT schema FROM events.parquet").show()
D) spark.sql("SELECT * FROM events.parquet").show()

3. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.withColumn()
B) DataFrame.filter()
C) DataFrame.groupBy().agg()
D) DataFrame.select()

4. 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.drop("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.dropfields("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")

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

Solutions: