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

Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.

From Spark documentation:

“Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table.”

DataFrame B (1 GB) fits within the default threshold and should be broadcasted.

It eliminates the need to shuffle the large DataFrame A.

Final Answer: B

In Spark SQL, the function date_sub(startDate, days) returns the date that is days before startDate.

If the days parameter is negative, Spark interprets it as subtracting a negative number, which effectively adds days to the date.

Example:

from pyspark.sql.functions import date_sub, lit

df = spark.createDataFrame([( "2024-10-01", )], ["dt"])

df.select(date_sub("dt", -5).alias("new_date")).show()

Output:

+----------+

| new_date |

+----------+

|2024-10-06|

+----------+

Why the other options are incorrect:

B: No error occurs; negative values are supported.

C: The start date changes if days ≠ 0.

D: Subtracting days would move the date backward, not forward.

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%.