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

1. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Reduce the value of spark.sql.shuffle.partitions
D) Increase the size of the dataset to create more partitions

2. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option wholetext=False to the text() function
B) Add the option wholetext=True to the text() function
C) Add the option lineSep=", " to the text() function
D) Add the option lineSep='\n' to the text() function

3. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.json("path/to/file", overwrite=True)
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.overwrite.json("path/to/file")
D) df.write.format("json").save("path/to/file", mode="overwrite")

4. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
B) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
C) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
D) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)

5. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The operation will fail if the Pandas DataFrame exceeds 1000 rows
B) The conversion will automatically distribute the data across worker nodes
C) Data will be lost during conversion
D) The operation will load all data into the driver's memory, potentially causing memory overflow

Solutions: