Serving, containers, and resources · Lesson 4 of 17 · 5 min

Inspect a container

Container inspection reveals the actual image, mounts, limits and network setup. Compare these effective settings with the intended deployment manifest.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-serving-resources-004.json. Separate the recorded facts from your assumptions.
Hint & worked example
Container inspection reveals the actual image, mounts, limits and network setup. Compare these effective settings with the intended deployment manifest.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-serving-resources-004.json
```
Common mistake to avoid: The source manifest alone proves which image is running.
2
Isolate the deciding signal
Select the running_image field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Reconcile the running r2 image with the intended r3 deployment.
A successful JSON query isolates running_image.
```
jq .running_image /opt/ai-lab/cases/aiops-serving-resources-004.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: reconcile-image-drift, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-serving-resources-004-decision.txt.
Hint & worked example
Reconcile the running r2 image with the intended r3 deployment.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'reconcile-image-drift' > /home/learner/lab/aiops-serving-resources-004-decision.txt
```
Common mistake to avoid: The source manifest alone proves which image is running.

Understand the pattern

Container inspection reveals the actual image, mounts, limits and network setup. Compare these effective settings with the intended deployment manifest.

Interpret the evidence

Reconcile the running r2 image with the intended r3 deployment.

A tempting mistake

The source manifest alone proves which image is running.

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-004.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "intended_image": "gateway:r3",
  "running_image": "gateway:r2",
  "mount_read_only": true
}

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

$ jq .running_image /opt/ai-lab/cases/aiops-serving-resources-004.json

"gateway:r2"

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

$ printf '%s\n' 'reconcile-image-drift' > /home/learner/lab/aiops-serving-resources-004-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 “reconcile-image-drift” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect a container” justify?Make the change now and gather the missing evidence afterward.Reconcile the running r2 image with the intended r3 deployment.The source manifest alone proves which image is running.

Apply it elsewhere

A colleague concludes: “The source manifest alone proves which image is running.” 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: Read container resource use Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision