Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Questions Tutorials

To create a new calculated column in Spark, use the .withColumn() method.

To remove an unwanted column, use the .drop() method.

Correct syntax:

from pyspark.sql.functions import col

employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")

.withColumn("tax", col("salary") * 0.1) → adds a new column where tax = 10% of salary.

.drop("age") → removes the age column from the DataFrame.

Why the other options are incorrect:

B: lit(0.1) creates a constant value, not a calculated tax.

C: .dropField() is not a DataFrame API method (used only in struct field manipulations).

D: Adds 0.1 to salary instead of calculating 10%.

The correct way to overwrite an existing file using the DataFrameWriter is:

df.write.mode("overwrite").json("path/to/file")

Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.

dedup_df = iot_bronze_df.dropDuplicates(["event_ts", "sensor_id", "metric_value"])

dropDuplicates accepts a list of columns to use for deduplication.

This ensures only unique records based on the specified keys are retained.

To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:

python

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df.orderBy("age", "salary", ascending=[True, False])

age will be sorted in ascending order

salary will be sorted in descending order

The orderBy() and sort() methods in PySpark accept a list of booleans to specify the sort direction for each column.

Documentation Reference: PySpark API - DataFrame.orderBy