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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. What is a feature of Spark Connect?

A) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C) It supports only PySpark applications
D) It has built-in authentication

2. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) replace
B) append
C) aggregate
D) complete

3. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option wholetext=True to the text() function
B) Add the option lineSep='\n' to the text() function
C) Add the option wholetext=False to the text() function
D) Add the option lineSep=", " to the text() function

4. A developer runs:

What is the result?
Options:

A) It appends new partitions to an existing Parquet file.
B) It stores all data in a single Parquet file.
C) It creates separate directories for each unique combination of color and fruit.
D) It throws an error if there are null values in either partition column.

5. 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)]

Solutions: