Databricks Associate-Developer-Apache-Spark-3.5

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) The code throws an error due to a schema mismatch.
B) [Row(name='alladin', age=20)]
C) [Row(name='bambi'), Row(name='alladin', age=20)]
D) [Row(name='bambi', age=None), Row(name='alladin', age=20)]

2. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and 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.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
C) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

3. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
B) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B

4. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A stage contains multiple jobs, and each job contains multiple tasks.
B) A stage contains multiple tasks, and each task contains multiple jobs.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A job contains multiple tasks, and each task contains multiple stages.

5. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
B) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
C) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
D) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))

Solutions: