CQE Practice Exam Questions with Answers Certified Quality EngineerExam Certification

?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.

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.

Implementing a supplier rating system provides multiple benefits:

Reduction in Supplier's Process Variability:

Focuses on improving consistency but is not the primary benefit.

Identify Competencies and Weaknesses:

Allows the organization to assess and benchmark suppliers' performance in different areas, identifying strengths and areas for improvement.

Helps in making informed decisions about supplier selection and development.

Measure Timeliness and Delivery:

Important aspect of supplier performance but not the primary benefit.

Improvement in Supplier Audit:

Enhanced audits through better data but secondary to understanding competencies and weaknesses.

Identifying competencies and weaknesses enables better supplier management and development, making it the primary benefit.

References: Supply chain management literature and supplier evaluation methodologies.

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.

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.

To convince management of the need for a quality improvement program, it is most effective to present the size of the problem in terms of potential savings and increased profit. Management is often driven by financial metrics, so quantifying the impact of quality issues and the benefits of improvements in monetary terms will be persuasive. Reference: "Juran's Quality Handbook" by Joseph M. Juran, which emphasizes presenting business cases in financial terms to gain management support for quality initiatives.

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 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

In value stream analysis, inspection is typically classified as non-value added. This is because inspection does not directly transform the product or add value from the customer's perspective. Instead, it is an activity that checks for defects or issues, which ideally should be prevented by improving the processes.

References:

Rother, M., & Shook, J. (1999). Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA. Lean Enterprise Institute.

Liker, J.K. (2004). The Toyota Way: 14 Management Principles from the World's Greatest Manufacturer. McGraw-Hill.

Pre-control charts are a simple form of process monitoring used primarily for short production runs or setups. Unlike traditional control charts, the limits of pre-control charts are not based on statistical calculations of process variation but are derived directly from specification limits.

Specifications:

Pre-control chart limits are determined by dividing the specification range into zones.

Typically, the specification limits are divided into green, yellow, and red zones to indicate areas of acceptable and unacceptable performance.

The green zone is typically the middle 50% of the specification range, yellow zones are the next 25% on either side, and the red zones are the outer 25%.

Natural Tolerance:

Refers to the spread of the process data within ±3 standard deviations from the process mean, but not used in pre-control charts.

Process Standard Deviation:

Used in traditional control charts to calculate control limits, not in pre-control charts.

Process Capability:

Evaluates how well a process can meet specifications, but pre-control limits are directly based on the specifications themselves.

References: Pre-control chart methodology and usage guidelines.

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.

When a potential solution, such as adding reinforcements to packaging, is identified, the most appropriate action is to pilot the solution. Piloting allows the company to test the effectiveness of the solution on a smaller scale before full implementation, ensuring that it resolves the issue without unintended consequences.

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.

According to ANSI ASQ Z1.4, switching from tightened inspection to normal inspection can occur when five consecutive lots have been accepted. This standard specifies the requirements for inspection by attributes, including the criteria for switching inspection levels based on the quality of consecutive lots.References:

ANSI/ASQ Z1.4-2003 (R2018): Sampling Procedures and Tables for Inspection by Attributes.

ASQ Quality Press: The Certified Quality Engineer Handbook.

During a brainstorming session, when dominant members are stifling others, having each team member suggest one idea in turn can ensure that all voices are heard. This structured approach prevents dominant members from monopolizing the discussion and encourages participation from quieter team members.

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.

The process performance index (Ppk) measures the performance of a process relative to the specification limits, accounting for the mean and variability of the process. It is an indicator of how well the process can produce output within the specification limits.

References:

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

ASQ. (n.d.). Process Performance (Pp, Ppk). Retrieved from https://asq.org/quality-resources/process-performance

P

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.

In a full-factorial design with three factors, each at two levels, the total number of runs required is calculated by 2n2^n2n, where nnn is the number of factors. For three factors, 23=82^3 = 823=8. Thus, 8 runs are required to cover all possible combinations of factor levels.

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.

?The control limits for an X?\bar{X}X? chart are calculated using the formula: UCL=X?+A2×RandLCL=X??A2×R\text{UCL} = \bar{X} + A_2 \times R \quad \text{and} \quad \text{LCL} = \bar{X} - A_2 \times RUCL=X?+A2?×RandLCL=X??A2?×R where X?\bar{X}X? is the process mean, A2A_2A2? is a constant based on the sample size, and RRR is the range.

?Given: X?=122\bar{X} = 122X?=122,?=2\sigma = 2?=2, n=5n = 5n=5.

?For n=5n = 5n=5, A2?0.577A_2 \approx 0.577A2??0.577.

?Calculating the control limits: UCL=122+(0.577×2)=122+1.154=123.154\text{UCL} = 122 + (0.577 \times 2) = 122 + 1.154 = 123.154UCL=122+(0.577×2)=122+1.154=123.154 LCL=122?(0.577×2)=122?1.154=120.846\text{LCL} = 122 - (0.577 \times 2) = 122 - 1.154 = 120.846LCL=122?(0.577×2)=122?1.154=120.846 After reviewing the choices, it appears there's an error in the given data or choices. Based on the provided options and standard calculations, the closest choice is:UCL=122+(0.577×10)=122+5.77=127.77(using a revised estimate if n is considered as 10)\text{UCL} = 122 + (0.577 \times 10) = 122 + 5.77 = 127.77 \quad \text{(using a revised estimate if n is considered as 10)}UCL=122+(0.577×10)=122+5.77=127.77(using a revised estimate if n is considered as 10) LCL=122?(0.577×10)=122?5.77=116.23\text{LCL} = 122 - (0.577 \times 10) = 122 - 5.77 = 116.23LCL=122?(0.577×10)=122?5.77=116.23 This may need further adjustment to fit the context, but C is closest to standard textbook methods.

Customers rate their suppliers to evaluate and compare the performance and quality of different suppliers. This helps in making informed decisions about which supplier to choose based on various factors such as quality, cost, and reliability. Among the given options, the most appropriate reason is:Reason: Comparing multiple sources of the same item allows the customer to choose the best supplier based on performance metrics.

The Student's t statistic is calculated as follows:

t = (X? - ?) / (s / ?n)

Given:

Sample mean (X?) = 515

Population mean (?) = 500

Sample standard deviation (s) = 9

Sample size (n) = 6

t = (515 - 500) / (9 / ?6) = 15 / (9 / 2.45) ? 3.73

References:

Montgomery, D.C., & Runger, G.C. (2010). Applied Statistics and Probability for Engineers.Wiley.

NIST/SEMATECH. (n.d.). e-Handbook of Statistical Methods. Retrieved fromhttps://www.itl.nist.gov/div898/handbook/

?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.

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)

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.

?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.

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.

?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.

Quality Function Deployment (QFD) is a structured approach for defining customer requirements and translating them into specific plans to produce products that meet those requirements.

Linking Customer Requirements to Product Features:

QFD starts by collecting and prioritizing customer needs (often referred to as the "voice of the customer").

These needs are then translated into design requirements, technical specifications, and product features.

The relationship between customer requirements and product features is mapped out in a QFD matrix, commonly known as the House of Quality.

Determining if Quality Procedures Are Followed:

This is part of quality assurance and auditing, not specifically QFD.

Determining Which Processes Are Functioning Correctly:

More related to process control and capability analysis, not QFD.

Reducing Costs of a Product and Product Features:

While cost reduction can be an outcome of QFD, it is not the primary focus. The primary focus is ensuring the product meets customer needs.

References: Quality Function Deployment methodologies and tools such as the House of Quality.

A critical design review (CDR) is an assessment of the product or system design's maturity, ensuring that it meets the specified requirements and is ready to proceed to the next development stage. It evaluates design performance, reliability, and readiness for production.

References:

INCOSE. (2015). Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities. Wiley.

NASA. (2016). Systems Engineering Handbook. NASA SP-2016-6105 Rev 2.

Risk describes the combination of the severity and probability of occurrence of harm. In the context of quality management, risk assessment involves evaluating both how severe the potential harm could be and the likelihood of it occurring, to manage and mitigate potential failures effectively.References:

Failure Mode and Effect Analysis (FMEA) by ASQ Quality Press.

ISO 31000: Risk Management Guidelines.

?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.

?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.

Management commitment is the most crucial aspect of a successful quality improvement effort. Without the active involvement and support of management, quality initiatives are less likely to receive the necessary resources, attention, and prioritization. Management commitment includes setting clear quality goals, providing the required resources, and leading by example to foster a culture of continuous improvement. This ensures that quality improvement is integrated into the organizational strategy and receives ongoing support and reinforcement.

References:

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

Crosby, P.B. (1979). Quality is Free. McGraw-Hill.

?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 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.

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 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.