Inference requests and APIs · Lesson 11 of 16 · 8 min

Compare batch and single requests

Batch execution has per-item outcomes. An accepted batch request does not mean every item completed successfully, so preserve item IDs and partial failure details.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-inference-apis-011.json. Separate the recorded facts from your assumptions.
Hint & worked example
Batch execution has per-item outcomes. An accepted batch request does not mean every item completed successfully, so preserve item IDs and partial failure details.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-011.json
```
Common mistake to avoid: Mark all ten items successful because the batch was accepted.
2
Isolate the deciding signal
Select the items_failed field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Inspect the one failed item while preserving the nine successful results.
A successful JSON query isolates items_failed.
```
jq .items_failed /opt/ai-lab/cases/aiops-inference-apis-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: inspect-failed-item, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-011-decision.txt.
Hint & worked example
Inspect the one failed item while preserving the nine successful results.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'inspect-failed-item' > /home/learner/lab/aiops-inference-apis-011-decision.txt
```
Common mistake to avoid: Mark all ten items successful because the batch was accepted.

Understand the pattern

Batch execution has per-item outcomes. An accepted batch request does not mean every item completed successfully, so preserve item IDs and partial failure details.

Interpret the evidence

Inspect the one failed item while preserving the nine successful results.

A tempting mistake

Mark all ten items successful because the batch was accepted.

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-inference-apis-011.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "batch_accepted": true,
  "items_succeeded": 9,
  "items_failed": 1
}

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

$ jq .items_failed /opt/ai-lab/cases/aiops-inference-apis-011.json

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

$ printf '%s\n' 'inspect-failed-item' > /home/learner/lab/aiops-inference-apis-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 “inspect-failed-item” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Compare batch and single requests” justify?Inspect the one failed item while preserving the nine successful results.Mark all ten items successful because the batch was accepted.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Mark all ten items successful because the batch was accepted.” 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.

Next: Read a streaming event shape Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision