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

1. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It can be used to interact with any remote cluster using the REST API
B) It allows for remote execution of Spark jobs
C) It provides a way to run Spark applications remotely in any programming language
D) It is primarily used for data ingestion into Spark from external sources

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

3. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
B) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
C) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

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

5. A developer runs:

What is the result?
Options:

A) It appends new partitions to an existing Parquet file.
B) It stores all data in a single Parquet file.
C) It throws an error if there are null values in either partition column.
D) It creates separate directories for each unique combination of color and fruit.

Solutions: