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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. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
B) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself

2. A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns for transaction_id, account_number, transaction_amount, and timestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

A) df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first("timestamp"))
B) df = df.dropDuplicates(["transaction_amount"])
C) df = df.dropDuplicates()
D) df = df.filter(F.col("transaction_id").isNotNull())

3. What is a feature of Spark Connect?

A) It supports only PySpark applications
B) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
D) It has built-in authentication

4. 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) Persist the users_vw data as a table
C) Recreate the users_vw and query the data using Spark
D) Query the users_vw using Spark

5. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
B) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
C) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
D) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()

Solutions: