How many objects reached the tray?
Use the contact record to separate a complete status from a stable placement.
The question
A fictional pick-and-place dashboard shows which jobs it marked complete. The brief requires at least 16 confirmed stable placements in the tray after release before the next review.
| Attempts | |
|---|---|
| Marked complete | 18 |
| All attempts | 20 |
The summary contains no attempt-level contact or outcome record.
“Can you report that the robot reached the placement target?”
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A strong answer, step by step
Open each step in turn. This is one strong answer, not the only one.
Step 1: Read the dashboard
What would you do here? Open the move you’d make.
Report the dashboard rate and ask for outcome evidence.
The strong move. The case target concerns observed stable placements. The dashboard gives a status count.
Say 90% of the objects were placed successfully.
Tempting, but it misses something. The completion flag does not show which objects remained in the tray after release.
See how a strong candidate says it
“The dashboard marks 18 of 20 complete, or 90%. I’d check what that flag means for the object in the tray.”
Why it works: The case target concerns observed stable placements. The dashboard gives a status count.
Step 2: Match the records
What would you do here? Open the move you’d make.
Count any contact reading near the release time.
Tempting, but it misses something. A nearby reading could belong to a different attempt and says little about whether the object stayed in the tray.
Check the attempt IDs and placement observations.
The strong move. The data need to belong to the attempt being counted. The observed result needs to cover the moment after release.
See how a strong candidate says it
“I’d ask for contact records and observed outcomes linked to each attempt ID.”
Why it works: The data need to belong to the attempt being counted. The observed result needs to cover the moment after release.
Step 3: Separate the outcomes
The review finds 5 flagged jobs with contact records from earlier attempt IDs. A separate review confirms 13 stable placements among the other flagged jobs and 2 misses among the unflagged jobs.
What would you do here? Open the move you’d make.
Report the range and the number of confirmations still needed.
The strong move. The stale readings leave the affected outcomes unknown. Treating them as known changes the answer without evidence.
Record the five stale cases as failed placements.
Tempting, but it misses something. A stale record removes evidence of the current attempt. It does not establish that the object missed the tray.
See how a strong candidate says it
“We have 13 confirmed placements, 5 unknowns and 2 misses. That gives 65% confirmed and a possible upper bound of 90%. We need 3 more confirmations to reach 16.”
Why it works: The stale readings leave the affected outcomes unknown. Treating them as known changes the answer without evidence.
Step 4: Ask for the missing evidence
What would you do here? Open the move you’d make.
Leave the target result open and explain which outcomes need evidence.
The strong move. The owner gets a count they can use now and can see what would close the gap.
Approve the next review on the chance that the best-case rate was 90%.
Tempting, but it misses something. The brief needs 16 confirmed placements. An upper bound is not a confirmation.
See how a strong candidate says it
“I’d tell the test owner we can confirm 13 placements. I’d ask for matched outcome evidence on the five unknown attempts before saying the target was met.”
Why it works: The owner gets a count they can use now and can see what would close the gap.
The answer, in short
- Decision
- Report 13 confirmed placements and leave the target open.
- Because
- Five stale records leave those placements unknown. Two other attempts are confirmed misses.
- What would change it
- Three more confirmed stable placements from the unknown attempts would meet the brief.
Common traps
- Counting software completion as observed placement.
- Calling an unknown attempt a miss.
Check your answer
- I read the dashboard rate.
- I linked records to attempt IDs.
- I bounded the possible count.
- I said what would meet the target.
Last reviewed Nov 11, 2026