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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. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_pandas()
B) psdf.to_pyspark()
C) psdf.to_dataframe()
D) psdf.to_spark()

2. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 80
B) 40
C) 160
D) 64

3. 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_pandas()
B) pdf.to_spark()
C) pdf.spark()
D) pdf.to_dataframe()

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

5. A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A) It is not able to handle deduplication in this scenario
B) It accepts watermarks in seconds and the code results in an error
C) It removes all duplicates regardless of when they arrive
D) It removes duplicates that arrive within the 30-minute window specified by the watermark

Solutions: