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

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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. A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
B) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
C) Execute their pyspark shell with the option --remote "sc://localhost"
D) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
E) Execute their pyspark shell with the option --remote "https://localhost"

2. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").distinct()
B) df.select("Name").orderBy(df["Name"].asc())
C) df.select("Name").distinct().orderBy(df["Name"].desc())
D) df.select("Name").distinct().orderBy(df["Name"])

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

4. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
B) Shuffle join because no broadcast hints were provided
C) Broadcast join, as df2 is smaller than the default broadcast threshold
D) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently

5. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
B) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
C) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D) A new SparkSession is created every time the getOrCreate() method is invoked.

Solutions: