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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 2: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 3: Structured Streaming | 10% | - Streaming Applications
|
| Topic 4: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 5: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 6: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 7: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
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 |



