Databricks Associate-Developer-Apache-Spark-3.5 exam - in .pdf

Associate-Developer-Apache-Spark-3.5 pdf
  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Aug 08, 2026
  • Q & A: 135 Questions and Answers
  • PDF Price: $59.99
  • PDF Demo

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  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Aug 08, 2026
  • Q & A: 135 Questions and Answers
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  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Aug 08, 2026
  • Q & A: 135 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionWeightObjectives
Structured Streaming10%- Defining streaming queries
- Fault tolerance and state management
- Output modes and triggers
- Streaming concepts and architecture
Developing Apache Spark DataFrame API Applications30%- User-defined functions (UDFs)
- Creating DataFrames and defining schemas
- Joining and combining datasets
- Partitioning and bucketing data
- Reading and writing data in various formats
- Handling missing values and data quality
- Filtering, sorting, and aggregating data
- Selecting, renaming, and modifying columns
Troubleshooting and Tuning Apache Spark DataFrame API Applications10%- Optimizing transformations and actions
- Managing memory and resource usage
- Identifying performance bottlenecks
- Debugging and logging
Using Pandas API on Apache Spark5%- Overview of Pandas API on Spark
- Converting between Pandas and Spark structures
- Key differences and limitations
Using Spark Connect to Deploy Applications5%- Running applications via Spark Connect
- Connecting to remote Spark clusters
- Spark Connect architecture
Apache Spark Architecture and Components20%- Execution hierarchy and lazy evaluation
- Execution and deployment modes
- Shuffling, actions, and broadcasting
- Fault tolerance and garbage collection
- Spark architecture overview
Using Spark SQL20%- Integrating Spark SQL with DataFrames
- Running SQL queries
- Using catalog and metadata APIs
- Working with functions and expressions

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

1. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

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


2. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

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


3. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A) Increase the size of the dataset to create more partitions
B) Reduce the value of spark.sql.shuffle.partitions
C) Enable dynamic resource allocation to scale resources as needed
D) Increase the value of spark.sql.shuffle.partitions


4. A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.[LINE 5] \
.load()
Options:

A) .option("subscribe.topic", "feed")
B) .option("subscribe", "feed")
C) .option("kafka.topic", "feed")
D) .option("topic", "feed")


5. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
D) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")


Solutions:

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

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