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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Structured Streaming | 10% | - Fault tolerance and state management
- Defining streaming queries
- Output modes and triggers
- Streaming concepts and architecture
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture
- Running applications via Spark Connect
- Connecting to remote Spark clusters
|
| Using Spark SQL | 20% | - Working with functions and expressions
- Running SQL queries
- Using catalog and metadata APIs
- Integrating Spark SQL with DataFrames
|
| Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection
- Execution and deployment modes
- Shuffling, actions, and broadcasting
- Spark architecture overview
- Execution hierarchy and lazy evaluation
|
| Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats
- Creating DataFrames and defining schemas
- User-defined functions (UDFs)
- Handling missing values and data quality
- Filtering, sorting, and aggregating data
- Partitioning and bucketing data
- Joining and combining datasets
- Selecting, renaming, and modifying columns
|
| Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark
- Converting between Pandas and Spark structures
- Key differences and limitations
|
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging
- Identifying performance bottlenecks
- Optimizing transformations and actions
- Managing memory and resource usage
|
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") \
.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") \
.trigger(processingTime="5 seconds") \
.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) 20
B) 1
C) 10
D) Same number as the cluster executors
3. 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 executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
C) It computes results immediately using eager execution.
D) It runs on a single node only, utilizing memory efficiently.
4. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?
A) spark.task.maxFailures
B) spark.sql.shuffle.partitions
C) spark.executor.cores
D) spark.executor.memory
5. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?
A) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
B) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
C) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
Solutions:
Question # 1
Answer: D | Question # 2
Answer: C | Question # 3
Answer: B | Question # 4
Answer: C | Question # 5
Answer: A |
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