CQE ASQ Certified Quality Engineer Exam Free Practice Exam Questions (2025 Updated)

Design for robustness focuses on creating products that can perform consistently under a variety of conditions and account for variations in production. By considering the variance in the part's production, the engineer ensures that the product design is tolerant to such variations, thereby making the product more reliable and durable.

References:

Taguchi, G., & Clausing, D. (1990). Robust Quality. Harvard Business Review.

Phadke, M.S. (1989). Quality Engineering Using Robust Design. Prentice Hall.

Failing to cite the significant contributions of other individuals in a final manuscript for publication violates the category of "Relations with Peers" in the ASQ Code of Ethics. This category emphasizes the importance of acknowledging and respecting the contributions of colleagues and collaborators in professional work. Proper citation practices are essential for maintaining professional integrity and fostering a collaborative environment within the professional community.References:

ASQ Code of Ethics.

Professional Ethics in Engineering by Mike W. Martin and Roland Schinzinger.

The primary purpose of the Material Review Board (MRB) is to review and determine the appropriate disposition of nonconforming products. The MRB evaluates the nonconformities, decides whether the products can be reworked, used as-is, or must be scrapped, and ensures that corrective actions are implemented to prevent recurrence.

References:

"The Certified Quality Engineer Handbook" by Connie M. Borror.

ASQ Glossary: Material Review Board (MRB)

If a value assigned to a unit is non-numerical, it is described as qualitative. Qualitative data describes attributes or characteristics that do not have a numerical value. Examples include descriptions such as color, type, or category.

References:

Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons.

ASQ (American Society for Quality) - Types of Data: ASQ Glossary

Instructing that all employees involved in creating those discrepancies be re-trained is considered a risk treatment. This action addresses the root cause of the non-conformities by improving the skills and knowledge of the employees, thereby reducing the likelihood of future occurrences.

An effective design review ensures that the design meets certain established criteria and standards. This helps in objectively evaluating the design to find any discrepancies or improvements needed. The other options are not necessarily true for every effective design review:

A: Design reviews can occur at various stages, not just when the design is complete.

B: While brainstorming might be part of the process, an effective review should have structure and defined criteria.

D: The review can involve multiple stakeholders, not just the development team.

In this scenario, the most appropriate action for the quality engineer is to expedite the review and disposition of the nonconforming material with the Material Review Board (MRB). This approach ensures that the nonconformance is assessed quickly by a team of experts, leading to an informed decision on whether the material can be used or needs further action. This aligns with ISO 9001:2015 requirements for handling nonconforming outputs and ensures that decisions are made based on a thorough review process. Reference: ISO 9001:2015 - Quality management systems -- Requirements, specifically the clauses dealing with nonconforming product management.

To find the probability of at least one car starting on a cold morning, we can use the complement rule.

Determine the probability of one car not starting:

Probability of a car starting: P(start)=0.6P(\text{start}) = 0.6P(start)=0.6

Probability of a car not starting: P(not start)=1−0.6=0.4P(\text{not start}) = 1 - 0.6 = 0.4P(not start)=1−0.6=0.4

Determine the probability of neither car starting:

Since the cars are independent, the probability of both cars not starting is: P(both not start)=0.4×0.4=0.16P(\text{both not start}) = 0.4 \times 0.4 = 0.16P(both not start)=0.4×0.4=0.16

Determine the probability of at least one car starting:

P(at least one starts)=1−P(both not start)=1−0.16=0.84P(\text{at least one starts}) = 1 - P(\text{both not start}) = 1 - 0.16 = 0.84P(at least one starts)=1−P(both not start)=1−0.16=0.84

References: Basic principles of probability and complement rule from standard probability texts.

To calculate the 95% confidence interval for the proportion of nonconforming items in a sample, we use the formula for the confidence interval for a proportion: p̂ ± Z * √(p̂(1-p̂)/n), where p̂ is the sample proportion (80/2000 = 0.04), Z is the Z-score for a 95% confidence level (1.96), and n is the sample size (2000). Plugging in the values, we get the confidence interval as [0.0308, 0.0499]. Reference: "Statistics for Engineers and Scientists" by William Navidi, which explains confidence intervals and their calculations.

To determine which problem area should be targeted for improvement first, we need to consider both the dollar savings and the probability of success. Calculating the expected savings (dollar savings multiplied by the probability of success) for each problem area gives us a clear picture:

A: 100,000 * 0.15 = 15,000

B: 92,000 * 0.30 = 27,600

C: 84,000 * 0.20 = 16,800

D: 72,000 * 0.33 = 23,760

Problem Area B has the highest expected savings (27,600), making it the best target for initial improvement efforts based on the given data. References:

Quality Management for Organizational Excellence by David L. Goetsch and Stanley Davis.

ASQ Certified Manager of Quality/Organizational Excellence Handbook.

The expected number of rejects per sample can be calculated using the reject rate and the sample size. Given a reject rate of 6% (or 0.06) and a sample size of 5:

Expected number of rejects per sample = 0.06 * 5 = 0.3

References:

Montgomery, D.C. (2009). Introduction to Statistical Quality Control. Wiley.

ASQ. (n.d.). Basic Quality Tools. Retrieved from https://asq.org/quality-resources/basic-quality-tools

In factorial design experiments, fractionating involves using a subset of the full set of experimental runs. A 242^424 full factorial experiment involves 16 runs, and fractionating this to 24−12^{4-1}24−1 results in 8 runs.

90% Fraction:

Incorrect, as this would imply a fractional design close to the full factorial, but it is not standard terminology.

75% Fraction:

The correct answer as 24−12^{4-1}24−1 implies a design that uses 816\frac{8}{16}168​ or 50% of the full factorial runs. However, in common terminology, a fractionated design resulting in fewer runs (half of the runs) is considered a 50% fraction. Therefore, this fractionation corresponds to a 50% fraction or half fraction.

Fraction:

Too vague, as it does not specify the fraction size.

Fraction:

Duplicates "fraction."

Therefore, the correct fractional reduction is to a 50% fraction, which is commonly used in design of experiments. The proper fraction designation in this case should be 50%, not 75%.

References: Design of experiments (DOE) principles.

A tree diagram is a tool used to break down broad goals into increasingly detailed levels. It starts with a central concept and branches out into finer levels of detail, similar to the structure of a tree. This helps in understanding the various components of a goal and how they relate to one another, facilitating a step-by-step approach to achieving the objective.

References:

ASQ (American Society for Quality). (n.d.). Tree Diagram. Retrieved from https://asq.org/quality-resources/tree-diagram

Tague, N.R. (2004). The Quality Toolbox. ASQ Quality Press.

If a quality engineer discovers a situation where unlabeled finished product could be shipped to customers, the appropriate preventive action is to form a team to error-proof the process. This involves identifying and eliminating the root causes of the issue to prevent recurrence, ensuring that processes are robust and error-free.

The most effective controls for mitigating risk are generally ranked as follows: 2. Design change: It is the most effective as it eliminates hazards at the source by redesigning the product or process.

Safety devices: These provide protection by incorporating safety mechanisms into the design but are secondary to eliminating the hazard.

Training: It is the least effective as it relies on human behavior and adherence to procedures.

References:

Manuele, F.A. (2014). Advanced Safety Management: Focusing on Z10 and Serious Injury Prevention. Wiley.

ISO 31000:2018. (2018). Risk Management – Guidelines. ISO.