CQE Certified Quality EngineerExam Questions and Answers

One of the primary advantages of a ship-to-stock program is that it reduces dock-to-line time. This program streamlines the process by allowing materials to be delivered directly to the production line without the need for incoming inspection, thus speeding up the supply chain and reducinginventory holding costs.References:

ASQ Quality Press: The Certified Quality Engineer Handbook.

Lean Supply Chain and Logistics Management by Paul Myerson.

A criticality analysis is used to classify or determine the safety and quality characteristics of a product or process. This analysis evaluates the severity, occurrence, and detection of potential failures, helping to prioritize issues that could significantly impact safety and quality.

References:

Stamatis, D.H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press.

IEC 60812:2018. (2018). Analysis techniques for system reliability – Procedure for failure mode and effects analysis (FMEA).

Robust design focuses on improving the quality of a product by minimizing the effects of variations without eliminating the causes of those variations.

Robust Design:

Techniques to make products or processes less sensitive to variations.

Aim to ensure performance under a wide range of conditions.

Tolerance Design:

Setting acceptable ranges of variation for product dimensions or performance metrics.

Design Verification:

Testing and validating that a design meets its requirements.

Design for Maintainability:

Designing products to be easy to maintain and repair.

Choosing design settings to minimize the effect of noise aligns with robust design principles.

References: Quality engineering and design optimization textbooks, particularly those discussing Taguchi methods and robust design principles.

Statistical Process Control (SPC) was originated by Walter A. Shewhart. Shewhart introduced the concept of control charts in the 1920s while working at Bell Laboratories. His pioneering work laid the foundation for the field of quality control and process management.References:

Statistical Method from the Viewpoint of Quality Control by Walter A. Shewhart.

ASQ Quality Press: The Certified Quality Engineer Handbook.

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.

In geometric dimensioning and tolerancing (GD&T), the symbol shown in the image is used to indicate a datum target point. This symbol is employed to designate specific locations on a part that are used as reference points for measurements. Datum targets are essential for ensuring accurate and repeatable measurements, particularly in complex assemblies where precision is crucial.

Datum Target Point: A specific point on a part that serves as a reference for measurement or manufacturing. Datum target points are critical for establishing a reference frame for the part's geometry.

References:

ASME Y14.5-2009 - Geometric Dimensioning and Tolerancing Standard.

Foster, L. W. (2010). Geometric Dimensioning and Tolerancing for Mechanical Design 2/E. McGraw-Hill Education.

A material review board (MRB) gets its authority to act from executive management. The MRB is typically composed of representatives from various departments, including engineering, quality, and production, and is responsible for reviewing and making decisions on non-conforming materials. The authority given by executive management ensures that the MRB has the power to enforce quality standards and make necessary decisions.

In software quality engineering, data can be classified as either numerical or non-numerical.

Numerical data can be measured and expressed in numbers, such as weight, height, and temperature.

Non-numerical data, also known as categorical data, represents characteristics or attributes that cannot be measured in numbers.

Such data is described as "qualitative" because it pertains to qualities rather than quantities.

References: The ASQ (American Society for Quality) defines qualitative data as data that approximates or characterizes but does not measure attributes, properties, or characteristics of a phenomenon.

A check sheet is a structured, prepared form for collecting and analyzing data.

It is one of the seven basic quality tools used to record frequency of events or defects, making it ideal for showing frequency distribution.

Affinity diagrams and interrelationship digraphs are used for organizing ideas and identifying relationships, while a PERT chart is used for project scheduling.

References: The ASQ and various quality management textbooks, such as "The Quality Toolbox" by Nancy R. Tague, outline the use of check sheets for frequency distribution analysis.

A product that exhibits a constant failure rate typically follows an exponential distribution for the failure rate. The exponential distribution is often used to model the time between failures in systems with a constant hazard rate, meaning the failure rate does not change over time.References:

Reliability Engineering by Elsayed A. Elsayed.

ASQ Quality Press: The Certified Reliability Engineer Handbook.

The purpose of the opening meeting for an audit is to conduct introductions, review the audit's scope and procedures, and clarify details of the audit plan. This meeting ensures that all participants understand the objectives, methodology, and expectations of the audit process.

The effectiveness of corrective action can best be assessed by measuring the results of the corrective action. This involves evaluating whether the action taken has successfully addressed theroot cause of the problem and has led to a measurable improvement in the quality metrics or process performance.References:

ASQ Quality Press: The Certified Quality Engineer Handbook.

Total Quality Management by Dale H. Besterfield.

An absolutely objective way to measure risk does not exist. Risk assessment inherently involves some level of subjectivity due to the uncertainty and variability in processes and outcomes. While various tools and metrics (e.g., lean metrics, six sigma metrics, quality function deployment) can aid in evaluating and managing risk, none provide an entirely objective measure. Reference: "Risk Management: Concepts and Guidance" by Carl L. Pritchard, which discusses the challenges of achieving absolute objectivity in risk assessment.

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

Document control is a critical element of a quality system that ensures that all work instructions, procedures, and specifications are up-to-date, consistent, and accessible to relevant personnel.Proper document control helps prevent errors and defects caused by outdated or conflicting work instructions.

References:

ISO 9001:2015. (2015). Quality Management Systems – Requirements. ISO.

ASQ. (n.d.). Document Control. Retrieved from https://asq.org/quality-resources/document-control

In risk management, after measures have been taken to prevent or control risks, some risks will still remain. These are known as residual risks.

Inherent Risk:

The risk present before any control measures are applied.

Residual Risk:

The risk that remains after preventive and control measures have been implemented.

It is the risk that the organization decides to accept and manage, rather than eliminating entirely.

Control Risk:

The risk that controls will fail to prevent or detect a risk.

Credit Risk:

The risk of a financial loss due to a borrower's failure to repay a loan.

Residual risk is the remaining risk after all control measures are applied.

References: Risk management frameworks and standards, such as ISO 31000.

Accelerated aging involves subjecting a product to elevated stress conditions (like temperature, humidity) to simulate the aging process.

Analyzing:

Involves reviewing data or conditions but not actively subjecting the product to stress.

Testing:

Involves actively evaluating the product under specific conditions to observe its performance.

Accelerated aging fits under testing because it involves placing the product under controlled stress conditions to observe its behavior over time.

Demonstrating:

Generally used to show functionality but does not specifically involve stress conditions.

Inspecting:

Involves looking at the product for defects or quality, not necessarily under accelerated conditions.

References: Product testing standards and reliability engineering texts.

A prioritization matrix is a tool that helps determine the best option from many choices based on their importance and merits. It evaluates and prioritizes different options using specific criteria and assigns scores to facilitate decision-making.

The Plan-Do-Check-Act (PDCA) cycle specifies the use of a feedback cycle. It is a continuous loop of planning, doing, checking, and acting, aimed at continuous improvement and problem-solving in quality management.

A laser scanner is a noncontact measurement tool that uses laser technology to measure the dimensions and shape of an object without physically touching it. This is useful for precise measurements where contact might damage the part or alter the measurement.

References:

Jain, R.K. (1994). Engineering Metrology. Khanna Publishers.

ASQ. (n.d.). Measurement System Analysis. Retrieved from https://asq.org/quality-resources/measurement-system-analysis

In the provided FMEA table, the potential failure mode "No solder" has the highest severity rating (S = 9), making it the most critical failure that could affect the customer by causing an open circuit. Higher severity indicates a more significant impact on the customer, thus making it the most likely to be seen by the customer.

The appropriate number of class intervals for the construction of a histogram can be estimated using Sturges' rule, which is given by k=1+3.322log⁡10(n)k = 1 + 3.322 \log_{10}(n)k=1+3.322log10​(n), where nnn is the number of observations. For 36 observations:

k=1+3.322log⁡10(36)≈1+3.322×1.556≈6.17k = 1 + 3.322 \log_{10}(36) \approx 1 + 3.322 \times 1.556 \approx 6.17k=1+3.322log10​(36)≈1+3.322×1.556≈6.17

This suggests around 6 intervals, but in practice, the number of intervals often rounded up, hence the appropriate number of class intervals would be 8 for better representation of the data. References:

Introduction to Probability and Statistics for Engineers and Scientists by Sheldon M. Ross.

ASQ Quality Press: The Certified Quality Engineer Handbook.

The P chart, also known as the proportion chart, is used to monitor the proportion of defective items in a process. For leaks in plastic bottles, which are attribute data (defective or not), the P chart is appropriate as it helps track the fraction of defective units over time.

References:

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

ASQ. (n.d.). P Chart. Retrieved from https://asq.org/quality-resources/p-chart

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 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.

When several units from a random sample are found to be defective, the immediate next action should be to segregate the defective units. This step is crucial to prevent defective units from being mixed with non-defective units, ensuring that they do not reach customers or proceed further in the production process. Segregating defective units helps in identifying the extent of the issue and facilitates further investigation and corrective actions.

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.

When collecting and summarizing data, the methods used for data collection can significantly affect the reliability and validity of the data.

Descriptive Statistics:

Concerned with summarizing data but not the method of collection.

Data Accuracy:

Ensured through proper collection methods but is an outcome rather than an influencing element.

Measurement Scales:

Refers to the types of data (nominal, ordinal, interval, ratio) but not directly affected by redundancy, versatility, and flexibility.

Data Collection Methods:

The techniques and processes used to gather data.

Ensuring redundancy, versatility, and flexibility in data collection methods can help in obtaining comprehensive and accurate data.

References: Research methodology and quality management texts.

ANSI ASQ Z1.4 provides sampling procedures and tables for inspection by attributes.

The sample size for Level II inspection is determined based on the lot size to be inspected.

This standard specifies how to choose sample sizes and acceptance numbers based on lot size to ensure statistically valid sampling.

References: ANSI/ASQ Z1.4-2008 "Sampling Procedures and Tables for Inspection by Attributes" explains how sample sizes are derived from lot sizes.

In the context of GD&T, the symbol "M" within a circle denotes Maximum Material Condition (MMC). MMC is a concept where a feature contains the maximum amount of material within its specified tolerance limits. This is often used to ensure that parts will fit together even when manufactured at their maximum permissible material limits.

Maximum Material Condition (MMC): The condition of a part feature which contains the greatest amount of material within the tolerance limits. For example, for a hole, it is the smallest diameter, and for a shaft, it is the largest diameter.

References:

ASME Y14.5-2009 - Geometric Dimensioning and Tolerancing Standard.

Neumann, B. (2009). Geometric Dimensioning and Tolerancing (GD&T). CreateSpace Independent Publishing Platform.

A frequency distribution summarizes how frequently each different value occurs in a dataset.

Stem-and-Leaf Plot:

Visualizes the shape of the data distribution.

Example: Data split into stems and leaves.

Box Plot:

Shows the distribution of data based on a five-number summary (minimum, first quartile, median, third quartile, maximum).

Pareto Chart:

Bar graph where factors are plotted in decreasing order of frequency along with a cumulative percentage line.

Frequency Distribution:

Table that displays the frequency of various outcomes.

Example: The given summary lists the number of customers for different time intervals.

The given data is a table that shows the number of customers corresponding to each time interval, making it a frequency distribution.

References: Statistics textbooks and resources on data visualization techniques.

To determine the portion of the product expected to meet specification, we use the process capability. Given:

Mean (X̄) = 101

Range (R) = 4

Sample size (n) = 5

Specification = 100 ± 3 (i.e., 97 to 103)

First, calculate the standard deviation (σ) using the average range (R̄) and a factor (d2) from standard tables for n = 5, where d2 = 2.326:

σ = R̄ / d2 = 4 / 2.326 ≈ 1.72

Next, convert the specification limits to Z-scores:

Lower Z = (97 - 101) / 1.72 ≈ -2.33 Upper Z = (103 - 101) / 1.72 ≈ 1.16

Using standard normal distribution tables, the area between these Z-scores corresponds to the portion within specification:

Portion within specification ≈ P(-2.33 < Z < 1.16) ≈ 0.954 or 95.4%

References:

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

ASQ. (n.d.). Process Capability Indices. Retrieved from https://asq.org/quality-resources/process-capability

The client, or the party commissioning the audit, has the primary responsibility for defining the purpose and scope of the audit.

The client sets the objectives, scope, and criteria to ensure the audit meets their specific needs and requirements.

The audit manager and auditor may assist in refining the scope and purpose but the initial determination lies with the client.

References: The ASQ "Auditing Handbook" and ISO 19011:2018 guidelines on auditing management systems outline the client's role in defining audit scope and objectives.

Design Failure Mode and Effects Analysis (DFMEA) is a systematic approach to identifying and addressing potential failure modes in a product design, aiming to reduce risks associated with those failures.

Severity Classification Matrix:

Used in DFMEA to prioritize risks based on their severity, occurrence, and detection, but not the most desirable outcome.

Reduction in Design Risk:

The primary goal of DFMEA is to identify potential failure modes and mitigate their impact, thereby reducing the overall risk in the design.

This involves taking corrective actions to eliminate or reduce the likelihood of failures and their effects.

Comprehensive List of Design Functions:

Part of the DFMEA process but not the ultimate goal.

Estimate of Failure Frequency:

Important for assessing risk but secondary to actually reducing the risk.

References: DFMEA methodologies and best practices in design risk management.

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.

Design Failure Mode and Effects Analysis (DFMEA) is a systematic method for evaluating a design to identify potential failure modes, their causes, and effects, and to prioritize the actions to mitigate these failures.

Identify Personnel Responsible for Implementation:

While important for action plans, it is not the primary focus of DFMEA.

Predict Failure Frequencies:

DFMEA does consider failure frequencies, but the primary goal is to address and mitigate failures.

Minimize Measurement Variation:

This is more relevant to process control and not the primary focus of DFMEA.

Mitigate Failure Modes:

The main objective of DFMEA is to identify potential failure modes and implement actions to reduce or eliminate the risks associated with these failures.

This involves analyzing the design to foresee where it might fail and taking preventive measures to improve reliability and safety.

References: Quality engineering and risk management texts, specifically those discussing the principles and objectives of DFMEA.

In written communication, using the active voice is most effective because it is clear and direct. Active voice makes it easier for the reader to understand who is doing what, leading to less ambiguity. For example, "The manager approved the request" is clearer than "The request was approved by the manager." Reference: "The Elements of Style" by Strunk and White, which advises using active voice for clarity and precision in writing.

Concurrent engineering is an approach to product development where multiple phases of the design process are carried out simultaneously to reduce time-to-market and improve product quality.

Concurrent engineering is a systematic approach to integrated product development that emphasizes the parallelization of tasks (i.e., performing tasks concurrently), which contrasts with the traditional sequential engineering design method.

When nonconforming items are found, the first action should be to identify and label them clearly. This prevents further processing or use and ensures proper handling of these items. Identification is critical before taking any further actions such as retesting, destruction, or returning to the supplier. Reference: ISO 9001:2015, Clause 8.7 - Control of Nonconforming Outputs, which specifies the need for clear identification and segregation of nonconforming products.

Risk control involves making decisions using information about acceptable levels of risk. This process helps ensure that the risks associated with various activities are managed within tolerable limits, balancing the potential impact with the organization's risk appetite.

Short-run Statistical Process Control (SPC) is particularly useful in situations where many different parts are made in small lots. This is because traditional SPC methods may not be effective when production runs are short and the volume of data for individual parts is limited. Short-run SPC techniques allow for effective monitoring and control of processes that produce a variety of parts in small quantities by standardizing and coding data to detect shifts and trends over multiple short runs.References:

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

ASQ Quality Press: The Certified Quality Engineer Handbook.

To collect data on the location of each leak on defective tanks, the most appropriate tool is a check sheet. A check sheet allows for the systematic recording and compiling of data, making it easy to identify and locate defects.Reason: A check sheet is used to collect data in a structured manner and is particularly useful for tracking the frequency and location of defects.

The Average Outgoing Quality Limit (AOQL) is a key concept in quality control and inspection plans. It is defined as the maximum average outgoing quality over all possible levels of incoming quality for a given acceptance sampling plan. The AOQL is particularly used in situations where 100% inspection or screening of rejected lots is performed, meaning nonconforming units are identified and replaced. This ensures that the outgoing quality is controlled even if the incoming quality fluctuates.

References:

"Introduction to Statistical Quality Control" by Douglas C. Montgomery.

ASQ (American Society for Quality) Glossary: AOQL

During the closing meeting of an external audit, a critical component is providing an explanation of the findings to ensure that all parties understand the audit results. This step is essential for clarity and sets the stage for discussing potential corrective actions and follow-up activities. Reference: "The ISO 19011:2018 Guidelines for Auditing Management Systems," which outlines the key components of audit closing meetings.

Work instructions are detailed documents that provide step-by-step guidance on how to perform specific tasks. They should include all necessary information to ensure the task is completed correctly and efficiently.

Work instructions should contain detailed procedures, including the equipment needed to perform the work. This ensures that employees have all the information required to complete their tasks properly.