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Databricks Certified Associate Developer for Apache Spark 3.5 - Python - Associate-Developer-Apache-Spark-3.5 real prep

Associate-Developer-Apache-Spark-3.5
  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Jul 23, 2026
  • Q & A: 135 Questions and Answers
  • PDF Version

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  • PDF Price: $59.98
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Using Spark SQL20%- Spark SQL Operations
  • 1. Joins and subqueries
  • 2. Filtering and sorting data
  • 3. Window functions
  • 4. Aggregations and grouping
  • 5. Built-in SQL functions
Topic 2: Using Spark Connect to Deploy Applications5%- Spark Connect
  • 1. Remote Spark sessions
  • 2. Client-server architecture
  • 3. Application deployment
Topic 3: Structured Streaming10%- Streaming Applications
  • 1. Output modes
  • 2. Streaming sources and sinks
  • 3. Structured Streaming concepts
  • 4. Triggers and checkpoints
Topic 4: Using Pandas API on Spark5%- Pandas API
  • 1. Pandas transformations
  • 2. Interoperability with PySpark
  • 3. Pandas on Spark DataFrames
Topic 5: Developing Apache Spark DataFrame API Applications30%- DataFrame Operations
  • 1. Handling null values
  • 2. Selecting and renaming columns
  • 3. Reading and writing data
  • 4. Partitioning data
  • 5. User Defined Functions
  • 6. Working with complex data types
  • 7. Creating and transforming DataFrames
Topic 6: Troubleshooting and Tuning10%- Performance Optimization
  • 1. Execution plan analysis
  • 2. Caching and persistence
  • 3. Shuffle optimization
  • 4. Broadcast joins
Topic 7: Apache Spark Architecture and Components20%- Spark Architecture
  • 1. Lazy evaluation
  • 2. Driver and Executor roles
  • 3. Adaptive Query Execution
  • 4. Cluster managers

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

1. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
C) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.start()


2. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) 1
B) 10
C) 20
D) Same number as the cluster executors


3. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
B) Increase the number of local threads based on the number of CPU cores.
C) Set the spark.executor.memory property to a large value.
D) Configure the application to run in cluster mode instead of local mode.


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


5. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 10
B) 5
C) 20
D) Same number as the cluster executors


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: B
Question # 4
Answer: D
Question # 5
Answer: C

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