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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q49-Q54):

NEW QUESTION # 49
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='alladin', age=20)]
C. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
D. The code throws an error due to a schema mismatch.

Answer: C

Explanation:
In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default. Instead, it returns null for fields that cannot be parsed correctly.
In the first row, "hello" cannot be cast to Integer for the age field → Spark sets age=None In the second row, "20" is a valid integer → age=20 So the output will be:
[Row(name='bambi', age=None), Row(name='alladin', age=20)]
Final answer: C

NEW QUESTION # 50
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A. Uses trigger(continuous='5 seconds') - continuous processing mode.
B. Uses trigger() - default micro-batch trigger without interval.
C. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
D. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

Answer: C

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
.outputMode("append")
.trigger(processingTime='5 seconds')
.start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.

NEW QUESTION # 51
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.parquet("/path/events/data/*")
D. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To read all files recursively within a nested directory structure, Spark requires therecursiveFileLookupoption to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under/path/events/data/and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option,inferSchemais irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within/path/events/data/and not subdirectories like
/2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set therecursiveFileLookupoption to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.

NEW QUESTION # 52
38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');
B. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');
C. CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
D. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');

Answer: D

Explanation:
To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.
Correct syntax:
CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
This creates an external table directly on the JSON data, inferring schema automatically and merging variations.
Why the other options are incorrect:
B: Missing schema merge configuration - fails with inconsistent files.
C: inferSchema applies to CSV/other file types, not JSON.
D: mergeAll is not a valid Spark SQL option.
Reference:
Spark SQL Data Sources - JSON file options (mergeSchema, path).
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - creating external tables and schema inference for JSON data.

NEW QUESTION # 53
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

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

Answer: B

Explanation:
A Spark job is triggered by an action (e.g., count, show).
The job is broken into stages, typically one per shuffle boundary.
Each stage is divided into multiple tasks, which are distributed across worker nodes.

NEW QUESTION # 54
......

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