Databricks-Spark-Assoc Structured Streaming Practice Question
What is the primary difference between a micro-batch streaming query and a continuous processing query in Spark?
⚠ Common exam trap
Candidates often confuse the terminology, wrongly assuming that continuous processing is the default mode or that it provides higher throughput rather than just lower latency at higher resource costs.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Continuous processing offers lower latency.
Micro-batch processing handles data in small discrete batches, providing high throughput and fault tolerance with second-level latency. Continuous processing, by contrast, runs a task continuously on each executor, enabling sub-millisecond latency. Choosing between them involves a trade-off between strict latency requirements and the operational complexity or feature limitations inherent in the continuous processing model, which does not support all operations yet.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Continuous processing supports all SQL operations.
Why it's wrong here
Continuous processing has limited support for advanced streaming features and SQL operations compared to micro-batch. Many operators are not yet compatible with continuous mode. Developers must verify that their specific transformations are supported before switching, as it is not a direct drop-in replacement for all micro-batch streaming queries.
- ✓
Continuous processing offers lower latency.
Why this is correct
Continuous processing is specifically engineered to provide sub-millisecond latency by avoiding the batch scheduling overhead present in micro-batch processing. By running tasks continuously on the executors, it eliminates the start-stop cycle, making it ideal for extremely latency-sensitive applications that require instantaneous response times to streaming data inputs.
- ✗
Micro-batch processing provides lower latency.
Why it's wrong here
Micro-batch processing is generally slower than continuous processing because it incurs the overhead of scheduling and managing batches at every interval. While sufficient for many use cases, it is not optimized for sub-millisecond latency, which is the primary value proposition of the continuous processing engine in Spark Structured Streaming.
- ✗
Continuous processing is the default mode.
Why it's wrong here
Micro-batch processing is the default mode for all Structured Streaming queries in Spark. Continuous processing must be explicitly enabled using the trigger settings. Misunderstanding this can lead to performance expectations that are not met, as the standard behavior is to operate in the well-understood, high-throughput micro-batch mode.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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