CQE Certified Quality EngineerExam Questions and Answers

Measurement scales are used to categorize and quantify variables.

Nominal Scale:

Used for labeling variables without any quantitative value. Categories are mutually exclusive and unordered.

Example: Inventory list of laptops categorized by brand or model.

Ordinal Scale:

Categorizes variables with a meaningful order but intervals are not equally spaced.

Example: Customer satisfaction ratings (poor, fair, good, excellent).

Interval Scale:

Ordered categories with equal intervals, but no true zero point.

Example: Temperature in Celsius.

Ratio Scale:

Ordered categories with equal intervals and a true zero point.

Example: Weight, height.

An inventory list of laptops in a sales office categorizes laptops into different groups without implying order or quantity, fitting the nominal scale.

References: Measurement theory and statistics textbooks.

A control chart is used to communicate performance data in a time sequence. It displays data points over time, allowing for the analysis of trends, shifts, and variations in the process. This tool is effective for monitoring process stability and performance. Reference: "Statistical Quality Control" by Eugene L. Grant and Richard S. Leavenworth, which details the use of control charts in quality control processes.

The diagram shown is typical of an interaction plot used in the analysis of designed experiments.

Confounded:

Confounding occurs when the effects of two factors cannot be separated. The diagram does not indicate this.

Replicated:

Replication refers to repeating the experiment or conditions multiple times to ensure reliability, not depicted here.

Show Randomization:

Randomization refers to the random assignment of treatments to experimental units. The plot does not indicate this.

Show Interaction:

Interaction occurs when the effect of one factor depends on the level of another factor.

The crossing lines in the plot indicate that the response to Factor A changes depending on the level of Factor B, demonstrating an interaction between the two factors.

References: Design of experiments (DOE) and statistical analysis texts.

The situation described is an example of an unintended consequence. Each module functions as intended individually, but when combined, the overall system's performance is suboptimal. This situation often arises when interactions between modules are not fully considered, leading to unforeseen issues that degrade overall system performance.

When establishing a system for the classification of defects, it is crucial to consider the relative seriousness of the defects. This involves evaluating the potential impact of each type of defect on the end-user and the product's performance. The classification typically divides defects into categories such as critical, major, and minor, based on their severity and potential consequences.

References:

"Quality Control Handbook" by J.M. Juran.

ISO 2859-1:1999, "Sampling procedures for inspection by attributes"

The authority for customer audits at a supplier’s facility is typically outlined in contracts or purchasing agreements.

These documents specify the terms and conditions under which the supplier agrees to allow audits by the customer.

Quality management system documents and certificates of conformance do not usually grant audit authority.

References: The ASQ "Auditing Handbook" and ISO 9001 standards emphasize that contractual agreements define the rights and obligations regarding audits.

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.

Given that components 1, 2, and 3 each have reliabilities of 0.95, and assuming the system configuration as depicted in the image (series-parallel configuration):

Reliability of the parallel components (2 and 3): Rparallel=1−[(1−R2)×(1−R3)]R_{\text{parallel}} = 1 - [(1 - R_2) \times (1 - R_3)]Rparallel​=1−[(1−R2​)×(1−R3​)] Rparallel=1−[(1−0.95)×(1−0.95)]R_{\text{parallel}} = 1 - [(1 - 0.95) \times (1 - 0.95)]Rparallel​=1−[(1−0.95)×(1−0.95)] Rparallel=1−[0.05×0.05]R_{\text{parallel}} = 1 - [0.05 \times 0.05]Rparallel​=1−[0.05×0.05] Rparallel=1−0.0025R_{\text{parallel}} = 1 - 0.0025Rparallel​=1−0.0025 Rparallel=0.9975R_{\text{parallel}} = 0.9975Rparallel​=0.9975

Reliability of the entire system (series of component 1 and the parallel system): Rsystem=R1×RparallelR_{\text{system}} = R_1 \times R_{\text{parallel}}Rsystem​=R1​×Rparallel​ Rsystem=0.95×0.9975R_{\text{system}} = 0.95 \times 0.9975Rsystem​=0.95×0.9975 Rsystem=0.947625R_{\text{system}} = 0.947625Rsystem​=0.947625

Rounded to three decimal places, the system reliability is 0.948.

The equation to calculate the upper control limit (UCL) for an average chart (X-bar chart) is X + D4 R. Here, X is the process mean, D4 is a control chart constant that depends on the sample size, and R is the average range of the samples. This formula helps in determining the upper threshold beyond which a process is considered out of control. Reference: "Introduction to Statistical Quality Control" by Douglas C. Montgomery, which provides detailed explanations on control charts and their calculations.

The chart provided is a control chart used to monitor process variation over time.

Control charts have upper control limits (UCL) and lower control limits (LCL) that represent the threshold of acceptable variation.

Points X and Y, which lie outside the control limits, indicate special causes of variation. These are deviations caused by specific, identifiable factors rather than inherent process noise.

Region Z, where data points are consistently below the mean and approaching the LCL, suggests a trend indicating a shift due to special causes.

References: According to ISO 9001 and the ASQ, control charts are utilized to detect special causes of variation, which signal that a process is out of control and requires investigation.

Robust design focuses on improving product and process designs to be less sensitive to variations, such as ambient temperature changes.

Adjusting machine settings to maintain consistent seal strength despite temperature fluctuations is an example of making a process more robust.

Robust design techniques aim to ensure that products and processes perform reliably under varying conditions.

References: "Taguchi's Quality Engineering Handbook" explains the principles of robust design and its application in manufacturing.

Risk management in software quality engineering aims to identify, assess, and mitigate risks that could affect project outcomes. The primary characteristic of effective risk management is that it should be all-inclusive. This means it should encompass all potential risk areas, including technical, project management, resource allocation, and external factors. An all-inclusive approach ensures that no significant risk is overlooked and that comprehensive strategies are in place to manage identified risks effectively.References:

Software Quality Assurance: Principles and Practice by Nina S. Godbole.

ASQ Software Quality Engineer Handbook.

When implementing Single Minute Exchange of Die (SMED), separating internal and external setup operations is a key technique. Internal setup operations are those that can only be performed when the machine is stopped, whereas external setup operations can be performed while the machine is running. By distinguishing and optimizing these operations, setup times can be significantly reduced.References:

Single Minute Exchange of Die: A Comprehensive Guide to SMED by Shigeo Shingo.

Lean Production Simplified by Pascal Dennis.

Robustness in statistical conclusions often refers to the ability to draw valid conclusions even when certain assumptions are violated. One of the critical factors for robustness is high statistical power, which refers to the probability of correctly rejecting the null hypothesis when it is false.Reason: High statistical power increases the likelihood that the test will detect a true effect if it exists, thereby supporting the validity of the conclusions.

Registration to ISO 9001 requires a third-party audit conducted by an accredited certification body.

Third-party audits are independent assessments to verify compliance with the ISO 9001 standard.

These audits ensure that the quality management system meets the requirements and is effectively implemented.

References: ISO 9001 guidelines and certification processes, as detailed in the "ISO 9001:2015 Quality Management Systems – Requirements" document.

Quality planning focuses on identifying and meeting the needs of the customers. It involves defining quality standards and determining how to achieve them to ensure customer satisfaction.

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.

Process capability analysis evaluates how well a process can produce output within specified limits.

It measures the inherent variability of a process relative to its specification limits.

This analysis is valuable for comparing the capabilities of different processes or equipment to ensure they meet required specifications.

It helps in selecting the most appropriate process or equipment by providing quantitative data on their performance.

References: ASQ's "Certified Quality Engineer Handbook" outlines the use of process capability analysis for comparing processes and equipment.

Option B, which is the oval or rounded rectangle shape, indicates a termination point on a flowchart. This symbol is typically used to represent the start or end of a process in flowchart diagrams.References:

ASQ Quality Press: The Certified Quality Engineer Handbook.

Flowcharting Techniques: A Guide to Better Communication of Information by Marvin P. Larkin.

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.

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.

Calculate the degrees of freedom for error:dfError=Total df−Treatment df=24−4=20\text{df}_{\text{Error}} = \text{Total df} - \text{Treatment df} = 24 - 4 = 20dfError​=Total df−Treatment df=24−4=20

Calculate the sum of squares for error:ssError=Total ss−Treatment ss=643−333=310\text{ss}_{\text{Error}} = \text{Total ss} - \text{Treatment ss} = 643 - 333 = 310ssError​=Total ss−Treatment ss=643−333=310

Calculate the mean squares:

MSTreatment=ssTreatmentdfTreatment=3334=83.25\text{MS}_{\text{Treatment}} = \frac{\text{ss}_{\text{Treatment}}}{\text{df}_{\text{Treatment}}} = \frac{333}{4} = 83.25MSTreatment​=dfTreatment​ssTreatment​​=4333​=83.25 MSError=ssErrordfError=31020=15.5\text{MS}_{\text{Error}} = \frac{\text{ss}_{\text{Error}}}{\text{df}_{\text{Error}}} = \frac{310}{20} = 15.5MSError​=dfError​ssError​​=20310​=15.5

Calculate the F-statistic:

F=MSTreatmentMSError=83.2515.5≈5.37F = \frac{\text{MS}_{\text{Treatment}}}{\text{MS}_{\text{Error}}} = \frac{83.25}{15.5} \approx 5.37F=MSError​MSTreatment​​=15.583.25​≈5.37

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.

A full factorial experiment involves testing all possible combinations of levels for each factor.

For a three-level (3 levels) and two-variable (2 factors) experiment, the number of runs is calculated as: 32=93^2 = 932=9

Therefore, the minimum number of runs required is 9.

References: "Design and Analysis of Experiments" by Douglas C. Montgomery discusses factorial design and the calculation of runs for different levels and factors.

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.

An ISO-compliant quality system requires a structured approach to identifying and addressing training needs.

Allowing Employees to Identify Their Own Training Needs:

This can lead to inconsistent and subjective training requirements.

Conducting a Needs Analysis for Each Job Category:

This method ensures that training is aligned with the specific requirements and responsibilities of each job role.

A needs analysis assesses the skills and competencies required for each job and identifies any gaps that training can address.

Requiring All Employees to Complete the Same Training Courses:

This approach may not address the specific needs of different job roles and can be inefficient.

Requiring Supervisors to Identify Training for Their Employees:

While supervisors can provide input, a comprehensive needs analysis is more effective for ensuring systematic and thorough identification of training needs.

References: ISO 9001:2015 standards and guidelines on training and competency.

Recording and distributing meeting minutes primarily serve to capture any decisions made during the meeting. This ensures that all participants have a clear understanding of the outcomes and action items agreed upon.

To detect subsurface flaws, a method that can penetrate the surface of the material and reveal flaws below the surface is required. Visual inspection and liquid penetrant tests are typically used for surface defects, while the Rockwell test measures hardness. Magnetic particle testing, however, is

Magnetic particle testing (MT) is a non-destructive testing method used to detect surface and near-surface flaws in ferromagnetic materials. It involves magnetizing the material and applying ferrous particles that gather around discontinuities, making subsurface flaws detectable.

Temperature measured in degrees centigrade is an example of an interval measurement scale. The interval scale is characterized by having equal intervals between measurements, but it does not have a true zero point. This means that while the difference between temperatures can be measured, there is no absolute zero where the absence of temperature is defined.References:

Fundamentals of Quality Control and Improvement by Amitava Mitra.

Measurement and Data Analysis for Engineering and Science by Patrick F. Dunn.

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.

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

In an unreplicated 232^323 factorial experiment, there are 8 runs (since 23=82^3 = 823=8). The total degrees of freedom are calculated as the number of runs minus 1, which is 8−1=78 - 1 = 78−1=7. However, considering there are three factors, each with one degree of freedom, and their interactions (1 for each main effect, 3 two-way interactions, and 1 three-way interaction), the correct total degrees of freedom are 7. References:

Design and Analysis of Experiments by Douglas C. Montgomery.

ASQ Quality Press: The Certified Quality Engineer Handbook.

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.

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.

The correlation coefficient measures the strength and direction of a linear relationship between two variables. A correlation coefficient of -1.0 indicates a perfect negative correlation, meaning that as one variable increases, the other decreases.

References:

Moore, D.S., McCabe, G.P., & Craig, B.A. (2017). Introduction to the Practice of Statistics. W.H. Freeman.

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

To ensure traceability from design and production through to end-user receipt and use, a company must develop a robust product identification system. This system involves assigning unique identifiers to each product or batch, which can be tracked throughout the entire supply chain. This allows for effective tracking of production history, quality control, and accountability in the event of product issues or recalls.

References:

"Traceability in the Food and Feed Chain" by IFSQN.

ISO 9001:2015, Quality Management Systems – Requirements

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.

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.