Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam With Confidence Using Practice Dumps

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 SparkSession is the entry point to Spark functionality in modern versions (2.x and later). It unifies the SparkContext, SQLContext, and HiveContext into a single object.

Best Practice:

Use one SparkSession for the entire application.

Create it once at the start using SparkSession.builder.getOrCreate().

Reuse it across all transformations and actions.

Stop it only after all operations are completed.

Example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("MyApp").getOrCreate()

# Perform transformations and actions

spark.stop()

Why the other options are incorrect:

B: SparkSession is the recommended interface; SparkContext alone is deprecated for SQL/DataFrame APIs.

C: Creating multiple sessions increases overhead and wastes resources.

D: Restarting SparkSession breaks lineage and adds unnecessary startup costs.

To view the schema of a Parquet file, you must use the DataFrameReader to load the Parquet data and call the .printSchema() method.

Correct syntax:

spark.read.parquet("events.parquet").printSchema()

This command loads the file metadata (without triggering a full read) and prints the column names, data types, and nullability information in a tree format.

Why the other options are incorrect:

A/D: SQL queries can’t directly introspect file schemas.

B: .show() displays data rows, not schema.