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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. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It filters the id values before performing the join.
C) It ensures that the join happens only when the id values are identical.
D) It increases the partition size for df1 and df2.

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

3. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

4. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Worker Nodes
B) Driver Nodes
C) CPU Cores
D) Executors

5. Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A) The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
B) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
C) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
D) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.

Solutions: