Let’s break this down carefully.
Key facts from the question:
- Pipeline mode: Development mode
- Trigger type: Triggered Pipeline Mode
- Table types:
- Two datasets:
STREAMING LIVE TABLE
- Three datasets:
LIVE TABLE (against Delta Lake sources)
- Previously unprocessed data exists.
- All definitions are valid.
1. Understanding Development mode in DLT
In Development mode:
- The pipeline performs one update of the DAG and then shuts down.
- Compute resources are kept alive for a period (default 30 minutes or so) to allow for iterative development — meaning you can start it again quickly without cluster startup time.
2. Understanding Triggered vs. Continuous in this context
- Triggered = Run once (in Production mode, that means process available data then stop; in Dev mode, same but compute persists for developer convenience).
- Continuous = Run forever (only available in Production mode).
Since this is Development mode + Triggered, the behavior is:
- Process all available data through the DAG once.
- Pipeline run stops (status: Completed).
- Compute does not terminate immediately — it stays available for the developer to run again without waiting for cluster startup.
3. Impact of mixed streaming and batch tables
STREAMING LIVE TABLE: In Triggered mode, these will process only new data since last run (or all data if first run).
LIVE TABLE (batch): In Triggered mode, these will run once.
All datasets are updated once in a single DAG execution, then the pipeline stops.
4. Matching to options
- A: Shuts down + compute terminated — ❌ Incorrect, because in Dev mode compute persists.
- B: Updates at intervals until shut down — ❌ That’s Continuous mode, not Triggered.
- C: Updates at intervals + compute persists after stop — ❌ Again, intervals imply Continuous.
- D: Updates once, pipeline shuts down, compute persists — ✅ Matches Dev + Triggered.
- E: Updates continuously, doesn’t shut down — ❌ That’s Continuous in Production mode.
Correct answer: D