Serving, containers, and resources · Lesson 3 of 17 · 9 min

List training containers

A container list shows process-level state and names. A running container may still contain an application that has not passed readiness checks.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-serving-resources-003.json. Separate the recorded facts from your assumptions.
Hint & worked example
A container list shows process-level state and names. A running container may still contain an application that has not passed readiness checks.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-serving-resources-003.json
```
Common mistake to avoid: Running and healthy are identical states.
2
Isolate the deciding signal
Select the health field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Wait for or investigate application health rather than equating running with ready.
A successful JSON query isolates health.
```
jq .health /opt/ai-lab/cases/aiops-serving-resources-003.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: wait-container-health, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-serving-resources-003-decision.txt.
Hint & worked example
Wait for or investigate application health rather than equating running with ready.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'wait-container-health' > /home/learner/lab/aiops-serving-resources-003-decision.txt
```
Common mistake to avoid: Running and healthy are identical states.

Understand the pattern

A container list shows process-level state and names. A running container may still contain an application that has not passed readiness checks.

Interpret the evidence

Wait for or investigate application health rather than equating running with ready.

A tempting mistake

Running and healthy are identical states.

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-serving-resources-003.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "container": "ai-gateway",
  "status": "running",
  "health": "starting"
}

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

$ jq .health /opt/ai-lab/cases/aiops-serving-resources-003.json

"starting"

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

$ printf '%s\n' 'wait-container-health' > /home/learner/lab/aiops-serving-resources-003-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 “wait-container-health” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “List training containers” justify?Running and healthy are identical states.Make the change now and gather the missing evidence afterward.Wait for or investigate application health rather than equating running with ready.

Apply it elsewhere

A colleague concludes: “Running and healthy are identical states.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Inspect a container Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision