Lean Six Sigma Black Belt Certification Lean Six Sigma Black Belt Certification 1 — Questions and Answers
Question 1: In a full factorial Design of Experiments (DOE) with 4 factors each at 2 levels, how many experimental runs are required?
- 8
- 16 (Correct answer)
- 32
- 64
Correct answer: 16
A full factorial DOE with k factors at 2 levels requires 2^k runs. With 4 factors: 2^4 = 16 runs. This ensures all combinations of factor levels are tested, allowing estimation of all main effects and interactions.
Question 2: A Gauge R&R study reports that the %Study Variation attributable to the measurement system is 28%. According to AIAG guidelines, how should this result be classified?
- Acceptable — variation is below the 30% threshold and requires no action
- Marginal — may be acceptable depending on the application and cost of improvement (Correct answer)
- Unacceptable — the measurement system must be replaced immediately
- Excellent — variation below 30% meets Six Sigma measurement standards
Correct answer: Marginal — may be acceptable depending on the application and cost of improvement
AIAG MSA guidelines classify measurement systems as: <10% = acceptable, 10–30% = marginal (acceptable based on importance of application and cost of the gauge), >30% = unacceptable. At 28%, the system is marginal and judgment is required before relying on it.
Question 3: What is the consequence of committing a Type II error (β error) in hypothesis testing during a DMAIC Analyze phase?
- Rejecting a true null hypothesis and incorrectly identifying a significant improvement
- Failing to detect a real effect and concluding no significant relationship exists (Correct answer)
- Setting the alpha level too high, increasing the chance of a false positive
- Accepting the alternative hypothesis when it has no practical significance
Correct answer: Failing to detect a real effect and concluding no significant relationship exists
A Type II (beta) error occurs when you fail to reject a false null hypothesis — you miss a real effect. In DMAIC, this means concluding an X variable has no significant impact when it actually does, causing the true root cause to be overlooked.
Question 4: In Value Stream Mapping, Process Cycle Efficiency (PCE) is calculated as:
- Total lead time divided by value-added time
- Value-added time divided by total lead time (Correct answer)
- Cycle time divided by takt time
- Throughput rate divided by defect rate
Correct answer: Value-added time divided by total lead time
PCE = Value-Added Time / Total Lead Time. A PCE below 25% typically signals a significant lean improvement opportunity. It quantifies how much of the total time a product spends in the value stream actually transforms it into something the customer pays for.
Question 5: A Black Belt is monitoring the count of defects per unit on a line where the number of units inspected per shift varies. Which control chart is most appropriate?
- p-chart
- c-chart
- u-chart (Correct answer)
- np-chart
Correct answer: u-chart
The u-chart tracks defects per unit when subgroup size varies. The c-chart also monitors defect counts but requires a constant subgroup size. The p-chart and np-chart track the proportion or number of defective units (pass/fail), not the count of individual defects.
Question 6: In the Kano model, which category describes product features that customers consider baseline requirements and whose absence causes extreme dissatisfaction, even though their presence does not increase satisfaction?
- Attractive (Delighters)
- One-dimensional (Performance)
- Must-be (Basic needs) (Correct answer)
- Indifferent
Correct answer: Must-be (Basic needs)
Must-be attributes are assumed by customers — they expect them without articulating them. Failing to provide them drives strong dissatisfaction, but fulfilling them simply meets the minimum bar and does not delight. Examples include a car that starts reliably or a hotel room that is clean.
Lean Six Sigma Black Belt Certification Lean Six Sigma Black Belt Certification 1
In a full factorial Design of Experiments (DOE) with 4 factors each at 2 levels, how many experimental runs are required?