Observability and tracing · Lesson 5 of 15 · 8 min

Inspect error-rate evidence

An error rate needs both failures and total eligible requests over a stated window. A count of failures without a denominator cannot show the rate.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-observability-tracing-005.json. Separate the recorded facts from your assumptions.
Hint & worked example
An error rate needs both failures and total eligible requests over a stated window. A count of failures without a denominator cannot show the rate.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-observability-tracing-005.json
```
Common mistake to avoid: Report 12 percent because there were 12 errors.
2
Isolate the deciding signal
Select the error_rate field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Report a 1.2 percent error rate for this 1000-request window.
A successful JSON query isolates error_rate.
```
jq .error_rate /opt/ai-lab/cases/aiops-observability-tracing-005.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: report-1-point-2-percent, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-observability-tracing-005-decision.txt.
Hint & worked example
Report a 1.2 percent error rate for this 1000-request window.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'report-1-point-2-percent' > /home/learner/lab/aiops-observability-tracing-005-decision.txt
```
Common mistake to avoid: Report 12 percent because there were 12 errors.

Understand the pattern

An error rate needs both failures and total eligible requests over a stated window. A count of failures without a denominator cannot show the rate.

Interpret the evidence

Report a 1.2 percent error rate for this 1000-request window.

A tempting mistake

Report 12 percent because there were 12 errors.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-observability-tracing-005.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "errors": 12,
  "requests": 1000,
  "error_rate": 0.012
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .error_rate /opt/ai-lab/cases/aiops-observability-tracing-005.json

0.012

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'report-1-point-2-percent' > /home/learner/lab/aiops-observability-tracing-005-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “report-1-point-2-percent” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect error-rate evidence” justify?Make the change now and gather the missing evidence afterward.Report a 1.2 percent error rate for this 1000-request window.Report 12 percent because there were 12 errors.

Apply it elsewhere

A colleague concludes: “Report 12 percent because there were 12 errors.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Follow a request trace Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision