🔴 CASE 1: “Clean Inspection vs Dirty History”

A pressure vessel shows:

• Recent inspection: no detectable damage
• Historical data: high corrosion rate trend
• Operating conditions: unchanged

Question: What is the BEST RBI action?

A. Reduce inspection interval
B. Trust latest inspection only
C. Maintain/increase inspection and reassess PoF
D. Ignore historical data

👉 Answer: C

Why this is tricky:
Candidates trust “clean inspection.”
👉 API expects you to trust trend over snapshot.

🔴 CASE 2: “Low Probability, Catastrophic Consequence”

A pipeline:

• Has very low PoF
• Runs through a densely populated urban area
• Carries flammable toxic fluid

Question: What is the correct RBI decision?

1. Reduce inspection due to low PoF
   B. Treat as low risk
   C. Prioritize due to high CoF
   D. Ignore consequence

👉 Answer: C

Trap:
Low PoF fools candidates.
👉 High consequence dominates risk.

🔴 CASE 3: “Conflicting Data Crisis”

Inspection results:

• UT shows wall thinning
• Visual inspection shows no visible damage
• Corrosion model predicts moderate degradation

Question: Best action?

1. Accept visual inspection
   B. Ignore UT results
   C. Investigate discrepancy and update RBI model
   D. Average results

👉 Answer: C

Why tricky:
Examiners test if you:
👉 Resolve conflicts, not choose sides.

🔴 CASE 4: “Post-Repair Risk Illusion”

Equipment underwent:

• Major repair and thickness restoration
• No update to RBI model
• Same risk ranking remains

Question: What should be done?

1. Accept existing risk ranking
   B. Reduce inspection automatically
   C. Update RBI model with new condition
   D. Remove from inspection program

👉 Answer: C

Trap:
Repair ≠ automatic risk reduction
👉 RBI must be recalculated.

🔴 CASE 5: “Poor Data, High Stakes”

An RBI study shows:

• Limited inspection history
• Uncertain corrosion rates
• Equipment in critical service (toxic + high pressure)

Question: What is the BEST approach?

1. Assume low risk due to lack of failures
   B. Reduce inspection due to lack of data
   C. Increase uncertainty factor and improve data quality
   D. Ignore uncertainty

👉 Answer: C

Why this is hard:
Candidates confuse “no data” with “no risk”
👉 API logic: uncertainty increases risk

🧠 What these 5 Questions test?

They cover the core failure points:

✔ 1. Trend vs snapshot thinking

✔ 2. Consequence dominance

✔ 3. Data conflict resolution

✔ 4. Dynamic RBI updates

✔ 5. Uncertainty handling

🔥 Master Rule (Exam winner)

When facing case-study questions:

👉 Always choose the answer that:

✔ Uses ALL data
✔ Reassesses the model
✔ Increases caution with uncertainty
✔ Prioritizes consequence
✔ Avoids assumptions