ASQ CQE Certified Quality EngineerExam Exam Practice Test

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.

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.

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.

Management philosophy is crucial in shaping the culture of an organization. It defines the values, beliefs, and principles that guide the behavior of its members. A strong, coherent management philosophy provides direction, motivates employees, and fosters a sense of identity and purpose within the organization. It influences decision-making, leadership styles, and the overall work environment.

The pre-production or pre-service planning process should include the preparation of product and process control plans. These plans outline the necessary steps and controls to ensure that the product meets quality requirements and that the process is capable of producing consistent results.References:

ASQ Quality Press: The Certified Quality Engineer Handbook.

Quality Planning and Analysis by J.M. Juran and Frank M. Gryna.

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.

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.

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.

In reliability testing, operating an item to stabilize its characteristics is known as burn-in. This process helps in identifying and eliminating early-life failures by running the equipment for a specified period under controlled conditions, thus ensuring more reliable performance in actual use.

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.

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.

Severity in the context of software quality and risk management refers to the extent of impact a failure would have on a system or process.

It assesses the consequences of a failure, regardless of its likelihood or detectability.

High severity indicates significant detrimental effects, such as critical system failures or safety hazards.

References: The ASQ and FMEA (Failure Mode and Effects Analysis) methodology define severity as the measure of the seriousness of the effect of a failure mode.

A Failure Mode and Effect Analysis (FMEA) is used to identify and prioritize potential failure modes in a system and their effects. The advantage of FMEA is its ability to communicate risk effectively to different stakeholders, ensuring that everyone is aware of potential issues and their impacts, facilitating better decision-making and risk mitigation.

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

The fishbone diagram, also known as the Ishikawa or cause-and-effect diagram, is used to identify and evaluate all possible causes of a problem.

It visually organizes potential causes into categories, helping to isolate the most probable ones.

This tool is effective for brainstorming and systematically analyzing the root causes of a quality issue.

References: ASQ's "Quality Toolbox" and Ishikawa's original works on cause-and-effect diagrams.

A control chart is used to communicate performance data in a time sequence. It is designed to plot data points chronologically to identify trends, variations, and patterns over time, which is essential for process monitoring and improvement. Reference: "Introduction to Statistical Quality Control" by Douglas C. Montgomery, which explains the application and interpretation of control charts in quality control.

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 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.

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.

Buy-in at all levels of management is critical for the success of quality initiatives. When all management levels are aligned and supportive of quality goals, it ensures that these initiatives are prioritized and integrated into daily operations. This comprehensive support fosters a culture of quality throughout the organization, making it easier to implement and sustain quality improvements.

References:

Juran, J.M., & Gryna, F.M. (1993). Quality Planning and Analysis: From Product Development through Use. McGraw-Hill.

Deming, W.E. (1986). Out of the Crisis. MIT Press.

Under a defect classification system, a nonconformance that could cause a product's usability to be substantially reduced would be classified as major. Major nonconformances are significant issues that affect the product's functionality, performance, or customer satisfaction but do not pose a safety risk or cause complete failure. Reference: ISO 9001:2015, which includes guidelines for classifying nonconformances based on their impact on product quality and usability.

Failure Mode and Effects Analysis (FMEA) is a preventive action methodology used to identify and mitigate potential failures before they occur.

Appraisal:

Involves measuring and evaluating products to ensure they meet quality standards (e.g., inspection and testing).

Prevention:

Involves activities designed to prevent defects from occurring.

FMEA falls under prevention as it proactively identifies potential failure modes and implements measures to mitigate them.

Control:

Involves monitoring processes to maintain quality, but does not directly relate to preventive analysis like FMEA.

Inspection:

Involves examining products or components to ensure they meet specifications, not preventive by nature.

References: Quality management and cost of quality frameworks.

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.

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.

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.

A scatter diagram is used to assess the relationship between two variables. In this case, it can be used to analyze the relationship between storage temperature and the overall length of the metalcomponent. A scatter diagram helps in visualizing any correlation between the two variables. Reference: "The Quality Toolbox" by Nancy R. Tague, which provides descriptions and uses for various quality tools including scatter diagrams.

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

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

For a normal distribution, the value of kkk that corresponds to the central 75% of the observations can be found using standard normal distribution properties.

Normal Distribution:

The total area under the normal curve is 1 (or 100%).

Central 75%:

This means the middle 75% of the distribution is within a range of ±k\pm k±k standard deviations from the mean.

The remaining 25% is split equally on both tails, so each tail has 12.5%.

Z-Scores:

The Z-score for 87.5% cumulative probability (100% - 12.5%) corresponds to the value of kkk.

Looking up the Z-score in standard normal distribution tables or using statistical software, we find Z≈1.28Z \approx 1.28Z≈1.28.

References: Statistical tables for normal distribution and probability theory texts.

In the provided drawing, the symbol marked "A" is being used to indicate a datum. A datum is a reference point, line, or surface used as a basis for measurement and to establish the location or orientation of other features. It serves as a starting point for dimensional control and ensures consistency in the manufacturing process.

The correct test statistic for a paired-sample t test is used when comparing the means of two related groups. The formula for the paired-sample t test is:

t=dˉsd/nt = \frac{\bar{d}}{s_d / \sqrt{n}}t=sd​/n​dˉ​

Where:

dˉ\bar{d}dˉ is the mean of the differences between the paired observations.

sds_dsd​ is the standard deviation of the differences.

nnn is the number of pairs.

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.

Error-proofing, or "poka-yoke," is a technique used to prevent errors by designing processes, equipment, or fixtures to avoid mistakes.

Fixture design involves creating jigs, fixtures, or tooling that ensure parts are correctly positioned and assembled, thereby preventing errors.

Housekeeping, equipment calibration, and double sampling do not inherently prevent errors by design.

References: The concept of error-proofing is detailed in quality engineering literature, such as "Poka-Yoke: Improving Product Quality by Preventing Defects" by Shigeo Shingo.

When an auditor finds a major discrepancy during a pre-award audit of a new supplier, the appropriate action is to include the discrepancy in the final report. This documentation is essential for transparency and for the organization to make informed decisions regarding the supplier. While other actions like verifying corrective actions and investigating the cause are important, they are typically follow-up steps after the discrepancy has been officially recorded.

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.

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.

FMEA prioritizes failure modes based on their Risk Priority Number (RPN), calculated as Severity x Occurrence x Detection.

Calculating RPN for each failure mode:

Failure Mode 1: 10 x 5 x 8 = 400

Failure Mode 2: 7 x 9 x 6 = 378

Failure Mode 3: 10 x 5 x 9 = 450

Failure Mode 4: 6 x 9 x 9 = 486

Failure Mode 3 has the highest RPN of 450, indicating it should be prioritized first for mitigation efforts.

References: FMEA guidelines, such as those from the AIAG (Automotive Industry Action Group), recommend addressing the highest RPN values first to reduce risk.

When developing a quality information system, the most important factor is understanding and addressing the needs of the end user. This ensures the system is functional, user-friendly, and meets the requirements for which it was designed. End-user needs dictate the system's features, usability, and overall effectiveness. Reference: ISO 9001:2015 - Quality management systems -- Requirements, which emphasizes the importance of customer focus in designing quality management systems.

A system audit evaluates the overall quality management system (QMS) of an organization to ensure it conforms to established standards, such as ISO 9001.

This type of audit examines whether the processes and activities are effectively integrated and implemented to achieve quality objectives.

It focuses on the adequacy and compliance of the entire QMS rather than individual processes or products.

References: ISO 9001 and the ASQ "Quality Audits for Improved Performance" emphasize the role of system audits in assessing conformance to quality programs.

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.

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.