Structured Streaming & Autoloader: Practice Questions
20 Inputs
40 Minutes
Intermediate

Industry
e-commerce
Skills
approach
data-storage
data-modelling
data-wrangling
stream-etl
data-understanding
Tools
databricks
spark
Learning Objectives
Validate your understanding of Structured Streaming syntax using readStream and writeStream
Test your ability to identify appropriate use cases for Auto Loader vs COPY INTO
Demonstrate knowledge of Auto Loader configuration including cloudFiles format and schema evolution
Assess your grasp of trigger modes for controlling streaming query execution frequency
Reinforce understanding of checkpointing for fault tolerance and incremental processing
Verify your knowledge of output modes and their impact on downstream data consumers
Overview
This practice set covers Structured Streaming & Auto Loader, essential components of the Databricks Certified Data Engineer Associate exam. Each question tests your understanding of streaming concepts, Auto Loader functionality, and real-time data processing patterns.
Test your knowledge of streaming and incremental ingestion:
- Structured Streaming fundamentals including readStream and writeStream operations
- Auto Loader capabilities for incremental file ingestion with schema evolution support
- Trigger modes including processingTime and availableNow for controlling execution frequency
- Checkpointing mechanisms for fault tolerance and tracking ingestion progress
- Output modes including append, update, and complete for different streaming scenarios
- Watermarking techniques for handling late-arriving data in time-based processing
- Auto Loader configuration options like maxFilesPerTrigger and schema location management
What makes this different:
- Real-world scenarios from e-commerce, transportation, and analytics domains
- Practical examples of Auto Loader vs COPY INTO decision-making
- Clear explanations of streaming concepts and configuration options
Use this practice set to validate your streaming and Auto Loader knowledge before exam day.
Prerequisites
- Understanding of Spark Structured Streaming concepts and syntax
- Familiarity with Auto Loader for incremental file ingestion
- Basic understanding of checkpointing and fault tolerance mechanisms