Six Sigma Green Belt Exam Questions and Test Answers

Green Belt Questions and Answers

Green Belt question and answer practice constitutes a core part of the training curriculum for Six Sigma certification. The leading process improvement methodology of Six Sigma is at heart a practical, applied discipline - yet certification surely also demands mastery of a detailed body of knowledge. Most training programs are anchored in the classic problem-solving approach of DMAIC, or Define, Measure, Analyse, Improve and Control.

This structured approach provides a flexible and reliable tool-kit and offers an armoury of concepts that can be flexibly applied in the workplace. Whereas Yellow Belt training introduces basic concepts such as 5S, Ishikawa diagrams and flowcharts, Green Belt training typically delivers a wider appreciation of statistical concepts such as hypothesis testing and designed experiments.

Six Sigma Green Belt certification varies by organisation and tuition provider but typically requires the completion of a short exam and the successful completion of at least one workplace project. The following Green Belt questions are designed to supplement and enhance the Six Sigma training experience. Green Belts are tasked to apply lean Six Sigma in their daily workplaces, so the emphasis is always on application rather than theory. For many, Green Belt training is an eye-opening experience: the potential for transformational quality improvement is everywhere. So above all, these questions should fire the imagination.


Green Belt Questions

1. What is a designed experiment? Answer in 25 words or less


2. Which of the following is not a benefit of a designed experiment?

(a) Identifies main and interaction effects

(b) Analyses different combinations of inputs

(c) Relatively low cost to implement

(d) Can be easily used to make improvements to random processes


3. Which of these defects per million opportunity (DPMO) levels approximates to four Sigma quality?

(a) 308,357

(b) 66,807

(c) 6,210

(d) 233

(e) 3.4


4. What is the difference between common cause and special cause variation?


5. What is a Gage R& R analysis?


6. In an effective measurement system, is repeatability or reproducibility likely to be a more significant source of variation?


7. Calculate the temperature main effect in this 2k factorial experiment.

Temp........Mat.......Yield

- 1 ............-1..........62 

  1............-1...........74

-1..............1...........56

1.............. 1.......... 70


8. According to Eli Goldratt’s Theory of Constraints, which is the optimal strategy?

(a) Identify the constraint, decide how to exploit it, subordinate production up to the level of the bottleneck, then seek to elevate the constraint.

(b) Stockpile inventory ahead of the bottleneck for maximum control of throughput.

(c) Constraints should never be elevated.

(d) Work simultaneously on eliminating all constraints.


9. Which of these is the best definition of “takt time?”

(a) Pace of a manufacturing system as determined by customer demand

(b) Average time taken to complete a task under normal operating conditions

(c) The maximum operating speed of a process or production line

(d) The long-run historic performance capacity of a system.


10. Which of these can form part of the “visual factory”?

(a) Yamazumi Boards

(b) Process Maps

(c) Kanban Signals

(d) All of the above

Green Belt Answers

1. A designed experiment involves varying input factors (Xs) to find out which variables, or combination of variables, deliver optimised output (Y).

2. Answer (d). It is hard to make improvements to a random process where the Green Belt has no ability to influence factor input levels.

3. 6,210 defects per million opportunities represent four sigma quality. Six Sigma quality levels approximate to 3.4 defects for every million repetitions of a process – world class quality.

4. All processes exhibit variation due to machines, materials, methods, measurement systems, people and nature. Common cause variation is long-term, random and predictable within the normal distribution of the process. Special cause variation is unusual, unpredictable and typically arises from excessive influence of one source of variation.

5. A Gage R&R analysis is a process to determine measurement system error. This has two components – repeatability (inherent variability of the measurement system measured by the same operator under identical conditions) and reproducibility (variability between operators when measuring the same item).

6. Typically human error is greater than machine error, so reproducibility is the larger component of measurement system variation.

7. Temp Effect = Average of 1 Yields less Average of -1 Yields

Temp Effect = (74+70)/2 less (62+56)/2 = 72 – 59 = 13

8. Goldratt suggested a five step process which involved identifying the constraint, working out how to get maximum output from the constrained process, subordinating the production level up to the bottleneck (to avoid a surplus of inventory building up) and then finally working to elevate (or eliminate) the constraint.

9. (a) is the best answer. Takt time sets the pace for manufacturing lines. In automobile manufacturing, for example, cars are assembled on a line, and are moved on to the next station after a certain time - the takt time. Takt time aims to match the pace of production with customer's demand.

10. All of the above - since these are all visual techniques. Yamazumi Boards are described here.

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Comments 1 comment

D.J. 3 years ago

Question 3 needs to specify whether the motorola shift is being taken into account. A 4 sigma quality level is around 63 DPMO without the shift.

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