Databricks Certified Associate Developer for Apache Spark 3.5 - Python - Associate-Developer-Apache-Spark-3.5 certification

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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. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It increases the partition size for df1 and df2.
C) It ensures that the join happens only when the id values are identical.
D) It filters the id values before performing the join.

2. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_dataframe()
B) pdf.spark()
C) pdf.to_pandas()
D) pdf.to_spark()

3. 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.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.drop("first_name", "last_name", "email", "birthdate")

4. 40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A) result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
B) result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
C) result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
D) result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()

5. 16 of 55.
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) Transformations are evaluated lazily.
B) Only actions trigger the execution of the transformation pipeline.
C) The Spark engine optimizes the execution plan during the transformations, causing delays.
D) The Spark engine requires manual intervention to start executing transformations.
E) Transformations are executed immediately to build the lineage graph.

Solutions: