ASQ CSSGB Lean Six Sigma Green Belt Certification exams

ASQ CSSGB (Lean Six Sigma Green Belt): This certification exam is not only a tricky one but also very costly, you have to dispense $438 as examination fee. And, if you don’t get through in the first attempt then any retake will again cost you $238! Do you really want to take this much risk without being confident of cracking it? The straight forward answer is NO, and that’s pretty wise decision.

We, at iDataSigma, have helped thousands of students to sail through the ASQ Green Belt (CSSGB), Black Belt (CSSBB) and Yellow belt (CSSYB) tests.

You’ll get here the bunch of practice papers which are very well designed keeping in mind the pattern and difficulty level being asked by ASQ CSSGB examiners.

There are 6 practice test papers (including one bonus paper) covering 124 questions which are aligned to the ASQ CSSGB BOK (Body of knowledge).

iDataSigma practice tests follow the levels of cognition as described by ASQ.

LEVELS OF COGNITION Based on Bloom’s Taxonomy—Revised (2001)

Complexity level of the test questions for the various topics in these practice tests is chosen as per ASQ CSSGB BOK. These levels are based on “Levels of Cognition” (from Bloom’s Taxonomy— Revised, 2001) and are presented below in rank order, from least complex to most complex

• REMEMBER | Recall or recognize terms, definitions, facts, ideas, materials, patterns, sequences, methods, principles, etc.

• UNDERSTAND | Read and understand descriptions, communications, reports, tables, diagrams, directions, regulations, etc.

• APPLY | Know when and how to use ideas, procedures, methods, formulas, principles, theories, etc.

• ANALYZE | Break down information into its constituent parts and recognize their relationship to one another and how they are organized; identify sublevel factors or salient data from a complex scenario.

• EVALUATE | Make judgments about the value of proposed ideas, solutions, etc., by comparing the proposal to specific criteria or standards. CREATE | Put parts or elements together in such a way as to reveal a pattern or structure not clearly there before; identify which data or information from a complex set is appropriate to examine further or from which supported conclusions can be drawn.