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

2. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

A) It optimizes by reading only the relevant subset of data from fewer partitions.
B) It compresses the data to save disk space.
C) It ensures that data is loaded into memory all at once for faster query execution.
D) It automatically cleans up unused partitions to optimize storage.

3. A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A) Spark Streaming, GraphX, and Pandas API on Spark
B) Spark DataFrames, Structured Streaming, and GraphX
C) Spark SQL, Pandas API on Spark, and Structured Streaming
D) Spark DataFrames, Spark SQL, and MLlib

4. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

A) It accepts watermarks in seconds and the code results in an error.
B) It removes duplicates that arrive within the 30-minute window specified by the watermark.
C) It is not able to handle deduplication in this scenario.
D) It removes all duplicates regardless of when they arrive.

5. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/")
C) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D) df = spark.read.parquet("/path/events/data/*")

Solutions: