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

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

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

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

3. What is the benefit of Adaptive Query Execution (AQE)?

A) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
B) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
C) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.

4. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.count()
B) streaming_df.select(countDistinct("name"))
C) streaming_df.filter("count < 30")
D) streaming_df.show()

5. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.dropna(how="all")
B) filtered_users = raw_users.na.drop("all")
C) filtered_users = raw_users.na.drop("any")
D) filtered_users = raw_users.dropna(how="any")

Solutions: