Serving, containers, and resources · Lesson 11 of 17 · 7 min

Review a deployment diff

Read a deployment diff for behavioral changes, not just additions and deletions. A replica change affects capacity and cost and should be reviewed against readiness and load.

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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-serving-resources-011.json. Separate the recorded facts from your assumptions.
Hint & worked example
Read a deployment diff for behavioral changes, not just additions and deletions. A replica change affects capacity and cost and should be reviewed against readiness and load.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-serving-resources-011.json
```
Common mistake to avoid: Replica count is cosmetic because the image did not change.
2
Isolate the deciding signal
Select the replicas_after field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Review the capacity and cost effect of increasing replicas from one to two.
A successful JSON query isolates replicas_after.
```
jq .replicas_after /opt/ai-lab/cases/aiops-serving-resources-011.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: review-replica-change, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-serving-resources-011-decision.txt.
Hint & worked example
Review the capacity and cost effect of increasing replicas from one to two.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'review-replica-change' > /home/learner/lab/aiops-serving-resources-011-decision.txt
```
Common mistake to avoid: Replica count is cosmetic because the image did not change.

Understand the pattern

Read a deployment diff for behavioral changes, not just additions and deletions. A replica change affects capacity and cost and should be reviewed against readiness and load.

Interpret the evidence

Review the capacity and cost effect of increasing replicas from one to two.

A tempting mistake

Replica count is cosmetic because the image did not change.

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-011.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "replicas_before": 1,
  "replicas_after": 2,
  "image_changed": false,
  "cost_reviewed": false
}

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

$ jq .replicas_after /opt/ai-lab/cases/aiops-serving-resources-011.json

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

$ printf '%s\n' 'review-replica-change' > /home/learner/lab/aiops-serving-resources-011-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 “review-replica-change” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Review a deployment diff” justify?Review the capacity and cost effect of increasing replicas from one to two.Replica count is cosmetic because the image did not change.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Replica count is cosmetic because the image did not change.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

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Keep learning

Case methodology: evidence, limits, decision