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

Section	Weight	Objectives
Troubleshooting and Tuning	10%	- Performance Optimization 1. Broadcast joins 2. Execution plan analysis 3. Caching and persistence 4. Shuffle optimization
Structured Streaming	10%	- Streaming Applications 1. Structured Streaming concepts 2. Triggers and checkpoints 3. Output modes 4. Streaming sources and sinks
Developing Apache Spark DataFrame API Applications	30%	- DataFrame Operations 1. Handling null values 2. Selecting and renaming columns 3. Partitioning data 4. User Defined Functions 5. Reading and writing data 6. Creating and transforming DataFrames 7. Working with complex data types
Using Spark Connect to Deploy Applications	5%	- Spark Connect 1. Client-server architecture 2. Application deployment 3. Remote Spark sessions
Using Pandas API on Spark	5%	- Pandas API 1. Interoperability with PySpark 2. Pandas on Spark DataFrames 3. Pandas transformations
Using Spark SQL	20%	- Spark SQL Operations 1. Joins and subqueries 2. Window functions 3. Filtering and sorting data 4. Built-in SQL functions 5. Aggregations and grouping
Apache Spark Architecture and Components	20%	- Spark Architecture 1. Lazy evaluation 2. Adaptive Query Execution 3. Driver and Executor roles 4. Cluster managers

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

Question 1

5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

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

Question 2

Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A. dropDuplicates with no arguments (removes based on all columns)
B. dropDuplicates on all columns (wrong criteria)
C. dropDuplicates on the exact matching fields
D. groupBy without aggregation (invalid use)

Question 3

29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A. Increase the driver memory on the client machine.
B. Switch the deployment mode to cluster mode.
C. Switch the deployment mode to local mode.
D. Add more executor instances to the cluster.

Question 4

10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A. It is available only with Python, thereby reducing the learning curve.
B. It computes results immediately using eager execution.
C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D. It runs on a single node only, utilizing memory efficiently.

Question 5

49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A. COMPLETE
B. AGGREGATE
C. APPEND
D. REPLACE

Solutions:

Question 1
Answer: C

Question 2
Answer: C

Question 3
Answer: B

Question 4
Answer: C

Question 5
Answer: A