LSS Green Belt · PeopleCert · Improve · Bank updated 2026-07-03
LSS Green Belt practice questions: Improve
5 free questions from 28 on this area · answer and explanation for each · no sign-up
These 5 questions come from the Improve section of our LSS Green Belt bank (28 questions on this area, which carries 20% of the real exam). Every question is original, with the correct answer explained and linked to the source it is drawn from.
1/5 · Improve · easy
In Lean Six Sigma, which term specifically refers to designing a process step or device so that an error is either physically impossible to make or is immediately and automatically detected before a defect is produced?
AKanban signal
BPoka-yoke (mistake-proofing)
CControl chart
DKaizen event
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2/5 · Improve
A team used a Fishbone diagram and 5 Whys to trace high customer complaint rates to an undocumented, inconsistent hand-off procedure between shifts. Which action best reflects proper root-cause-to-improvement linkage in the Improve phase?
AIncrease the sampling frequency in the control chart monitoring complaint counts.
BDesign and pilot a standardized shift hand-off checklist that directly addresses the identified root cause.
CRetrain all employees company-wide on general customer service skills to be safe.
DAdd an additional inspection step at the end of the process to catch complaints before they reach the customer.
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B is correct. Root-cause-to-improvement linkage means the solution selected must directly eliminate or mitigate the specific root cause validated during analysis, not a generic or downstream fix. Here, the root cause is a missing standardized hand-off procedure, so the improvement action should create and pilot that standard, rather than adding inspection, generic training, or increased monitoring.
↗ the IASSC Lean Six Sigma Green Belt Body of Knowledge (ILSSBOK, PeopleCert syllabus) — Improve
3/5 · Improve · hard
A scatter plot of two continuous process variables produces a correlation coefficient of r = −0.85. Which statement correctly interprets this result?
AThere is only a weak negative relationship, because the magnitude is less than 1.0.
BOne variable must directly cause the other to decrease.
CThere is a strong negative linear relationship: as one variable increases, the other tends to decrease.
DThe variables are unrelated, since a negative sign indicates the absence of any relationship.
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4/5 · Improve · hard
A Green Belt runs a 2² full factorial DOE (4 experimental runs) to study weld defects, varying weld temperature (Low/High) and weld speed (Low/High). The results show that raising temperature sharply reduces defects when speed is Low, but has almost no effect on defects when speed is High. What does this pattern most directly indicate?
AA significant main effect for temperature, meaning temperature affects defects the same way regardless of speed.
BA significant interaction effect between temperature and speed, meaning the effect of one factor depends on the level of the other factor.
CThe experiment is confounded, so no conclusions about either factor can be drawn.
DNeither factor is statistically significant because their combined effect is inconsistent.
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B is correct. In a 2² full factorial design (2 factors, 2 levels each, 4 runs), an interaction exists when the effect of one factor depends on the level of the other factor. Here, temperature's impact on defects changes depending on whether speed is Low or High, which is the classic signature of an interaction effect — a core concept in basic DOE awareness alongside factors and levels.
↗ the IASSC Lean Six Sigma Green Belt Body of Knowledge (ILSSBOK, PeopleCert syllabus) — Improve
5/5 · Improve · hard
A Green Belt is investigating how oven temperature and belt speed jointly affect the crack rate of a baked component. A colleague suggests testing one factor at a time (OFAT), holding the other factor constant, instead of running a full factorial design. What is the main limitation of the OFAT approach in this situation?
AOFAT requires more total experimental runs than a comparable full factorial design covering the same two factors and levels
BBecause only one factor changes per run in OFAT, the run order does not need to be randomized
COFAT cannot detect the interaction between temperature and belt speed, so a setting found by adjusting one factor while holding the other fixed may not be the true optimum combination
DOFAT is invalid unless the crack rate response is first verified to be normally distributed
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C is correct. A full factorial design varies factors together across all combinations, allowing estimation of interaction effects — for example, whether belt speed's effect on cracking depends on oven temperature. OFAT only estimates each factor's main effect in isolation and can therefore settle on a suboptimal combination by missing a synergistic or antagonistic interaction between the factors.
↗ the IASSC Lean Six Sigma Green Belt Body of Knowledge — Improve
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