Alert Config LabLearn how alert conditions work · Beginner exercise with answers
Missing data vs. threshold violations
A threshold condition evaluates whether reported values are too high or too low. An absence condition evaluates how long no new data has arrived. Missing data is not a normal zero.
Make a prediction
Is receiving a 0% reading the same as receiving no new point? Predict when the absence condition becomes true and when it clears in this table.
A fixed, small input example
Distinguish an observed 0% from missing data and inspect a MetricAbsence evaluation.
| Time | State |
|---|---|
| 0 s | Observed 0% |
| 60–180 s | No data |
| 240 s | Resumes at 0% |
| 300 s | Observed 0% |
Fixed public teaching seed: 270927 · The dataset is fixed by the table above; it contains no personal settings.
How values flow into the evaluation
- Observed 0%, a gap, then data resumes
- MetricAbsence: evaluate missing observations, not numeric zero
- Becomes true after 120 seconds and clears when points resume
Conditions held fixed in this example
- MetricAbsence with a 120-second retest. This is separate from missing-data handling on a threshold condition.
- This synthetic fixture is independent of the app's current settings.
fixed-teaching-points · v1 · 60 seconds sample interval · 300 seconds horizon
Answer and explanation
Reveal the answer
In this evaluator, absence becomes true at 120 seconds and clears when data resumes at 240 seconds. An observed 0% alone is not absence.
0% is an observed value, not missing data. In this fixture's evaluator, absence becomes true 120 seconds after the last observation. It clears when a new point arrives at 240 seconds. The table does not model real ingestion delays or notification timing.
Explore in the simulator
This is a separate experiment using your current settings, not an import of the fixed table. Review your rule first and, if needed, build a practice waveform before trying these steps.
- Check whether the current condition is MetricThreshold or MetricAbsence. Existing rules will not be replaced automatically.
- Use synthetic data with missing points and sufficient observation history when exploring an absence rule.
- Distinguish data resuming, insufficient initial history, and unevaluable conditions. Consult the official documentation for actual service behavior.
Opening the app shows a confirmation dialog. The link alone does not change settings, waves, or saves. If you start a teaching test, the original experiment is protected in a separate browser-storage area. The fixed table above and the simulator's test for your current rule are separate datasets.
Check your understanding
- Explain the difference between a zero value and no observation.
- Distinguish MetricAbsence from missing-data handling on a threshold condition.
- Check data absence, insufficient initial history, and unevaluable conditions separately.
For a comparison note, record the dataset ID, changed setting, and opening time or reason no alert opened. Save the experiment explicitly in the app if you want to return to it.
Scope and limits
This teaching fixture explicitly supplies missing points. It does not model real ingestion delay, service evaluation timing, or notification channels.
Educational comparisons use synthetic data. They do not guarantee production detection rates or notification timing, and do not connect to or configure Google Cloud. Unsupported operations and insufficient history can be unevaluable.
Official references
Reviewed September 27, 2026. Not an official or affiliated Google service.
Return to the simulator
← Back to the simulatorIf you opened this guide in a new tab from the app, close this tab to continue in the original tab. The link above opens the simulator again. To resume the same waveform and playback position later, use Save / open experiment in the app. This is separate from settings autosave.