• There is no stigma to not passing. The mere fact that you prepared for and took the exam has enhanced your own knowledge and skill in the quality profession.

I.  MANAGEMENT AND LEADERSHIP IN QUALITY ENGINEERING (19 Questions)

1. Professional Conduct and ASQ Code of Ethics
2. Management systems for improving quality (e.g., policy deployment, benchmarking, goal setting, planning and scheduling, project management, quality information systems)
3. Leadership principles and techniques (e.g., leading quality initiatives, team development, team building, team organization)
4. Facilitation principles and techniques, (e.g., roles and responsibilities, conflict resolution)
5. Training (e.g., needs analysis, program development, material construction, determining effectiveness)
6. Cost of quality (e.g., concepts, data collection, and reporting)
7. Quality philosophies and approaches (e.g., Juran, Deming, Taguchi, Ishikawa)
1. Benefits of quality
2. History of quality
3. Definitions of quality
8. Customer relations, expectations, needs, and satisfaction (e.g., QFD, customer satisfaction surveys)
9. Supplier relations and management methodologies (e.g., qualification, certification, evaluation, ratings, performance improvement)

II.  QUALITY SYSTEMS DEVELOPMENT, IMPLEMENTATION, AND VERIFICATION (19 Questions)

1. Elements of a quality system
2. Documentation systems (e.g., configuration management, document control)
3. Domestic and/or international standards and/or specifications
4. Quality audits
1. Types and purpose of quality (e.g., product, process, system, registration, certification, 1st party, 2nd party, 3rd party, management, compliance)
2. Roles and responsibilities of individuals involved in the audit process (e.g., audit team, client, auditee)
3. Quality Audit Planning, Preparation, and Execution
4. Audit reporting and follow-up (e.g., need for corrective action and verification)

III.  PLANNING, CONTROLLING, AND ASSURING PRODUCT AND PROCESS QUALITY (33 Questions)

1. Preproduction or pre-service planning process
1. Classification of quality characteristics
2. Design inputs and design review
3. Validation and qualification methods
4. Interpretation of technical drawings and specifications
5. Determining product and process control methods
2. Material Control
1. Material identification, status, and traceability
2. Sample integrity (e.g., avoiding contamination or misidentification)
3. Material segregation
4. Material Review Board (MRB)
3. Acceptance Sampling
1. General concepts (e.g., lot-by-lot protection, average quality protection, producers and consumers risk, operating characteristics [OC] curves)
2. Definitions (AQL, LTPD, AOQ, AOQL)
3. Standards (ANSI/ASQC Z1.4, ANSI/ASQC Z1.9)
4. Acceptance sampling plans (single, double, multiple, sequential, continuous)
4. Measurement Systems
1. Terms and definitions (e.g., precision, accuracy, metrology)
2. Destructive and nondestructive measurement and test methods
3. Selection of measurement tools, gages, and instruments
4. Measurement system analysis (e.g., repeatability and reproducibility, measurement correlation, capability, bias, linearity)
5. Metrology (traceability to standard, measurement error, calibration systems, control of standards and integrity)

IV.  RELIABILITY AND RISK MANAGEMENT (11 Questions)

1. Terms and definitions (e.g., MTTF, MTBF, MTTR, availability, failure rate)
2. Reliability life characteristic concepts (e.g., bathtub model)
3. Design of systems for reliability (redundancy, series, parallel)
4. Reliability and maintainability
1. Prediction
2. Prevention
3. Maintenance Scheduling
5. Reliability failure analysis and reporting
6. Reliability / Safety / Hazard Assessment Tools
1. Failure mode and effects analysis (FMEA)
2. Failure mode and effects criticality analysis (FMECA)
3. Fault-tree analysis (FTA)

V.  PROBLEM SOLVING AND QUALITY IMPROVEMENT (25 Questions)

1. Approaches (e.g., Kaizen, CI, PDSA)
2. Management and planning tools (affinity diagrams, tree diagrams, process decision program charts, matrix diagrams, interrelationship digraphs, prioritization matrices, and activity network diagrams)
3. Quality tools (Pareto charts, cause and effect diagrams, flowcharts, control charts, check sheets, scatter diagrams, and histograms)
4. Corrective action (problem identification, correction, recurrence control, effectiveness assessment)
5. Preventive action (e.g., error proofing, opportunities for improvement, robust design)
6. Overcoming barriers to quality improvement

 

VI.  QUANTITATIVE METHODS (53 Questions)

1. Concepts of Probability and Statistics
1. Terms (e.g., population, parameter, statistic, random sample, expected value)
2. Drawing valid statistical conclusions (e.g., enumerative and analytical studies, assumptions and robustness)
3. Central limit theorem and sampling distribution of the mean
4. Basic probability concepts (e.g., independence, mutually exclusive, multiplication rules, complementary probability, joint occurrence of events)
2. Collecting and Summarizing Data
1. Types of data (continuous vs. discrete; variables vs. attributes)
2. Measurement scales (nominal, ordinal, interval, ratio)
3. Methods for collecting data (e.g., check sheets, coding data, automatic gaging)
4. Techniques for assuring data accuracy and integrity
5. Descriptive statistics (measures of central tendency, measures of variation, frequency distribution, cumulative frequency distribution)
6. Graphical Methods
1. Depicting relationships (e.g., stem-and-leaf plots, box-and-whisker plots, run charts, scatter diagrams)
2. Depicting distributions (e.g., histogram, normal probability plot, Weibull plot)
3. Properties and Applications of Probability Distributions
1. Discrete distributions (binomial, Poisson, hypergeometric, multinomial)
2. Continuous distributions (uniform, normal, bivariate normal, exponential, log normal, Weibull, Chi-square, Studentís t, F-distribution)
4. Statistical Decision-Making
1. Point and interval estimation (efficiency and bias of estimators, standard error, tolerance intervals, confidence intervals)
2. Hypothesis testing
1. Tests for means, variances, and proportions
2. Significance level, power, type I and type II errors
3. Statistical versus practical significance
3. Paired comparison
4. Goodness-of-fit tests
5. Analysis of Variance (ANOVA)
6. Contingency tables
5. Measuring and Modeling Relationships Between Variables
1. Simple and multiple least-squares linear regression (e.g., calculate and use the regression model for estimation and inference, interpret regression statistics)
2. Simple linear correlation (e.g., calculate and interpret the correlation coefficient, perform hypothesis, test and calculate confidence interval for the correlation coefficient)
6. Designing Experiments
1. Terminology (e.g., independent and dependent variables, factors and levels, response, treatment, error, replication)
2. Planning and organizing experiments (e.g., objective, choice of factors and responses, defining measurement methods, choice of design)
3. Design principles (power and sample size, balance, replication, order, efficiency, randomization and blocking, interaction, confounding)
4. Design and analysis of one-factor experiments (e.g., completely randomized, randomized block)
5. Design and analysis of full factorial experiments
6. Design and analysis of two-level fractional factorial experiments
7. Taguchi robustness concepts
7. Statistical Process Control (SPC)
1. Objectives and benefits
2. Selection of variable
3. Rational subgrouping
4. Selection and application of control charts (x-bar & r, x-bar & s, individual and moving range [ImR], moving average and moving range [MamR], median, p, np, c, u)
5. Analysis of control charts (common vs. special causes of variation and rules for determining statistical control)
6. Pre-control
7. Short-run SPC
8. Analyzing Process Capability
1. Designing and conducting process capability studies
2. Calculating process performance vs. specification
3. Process capability indices (C_p, C_pk, C_pm, CR)
4. Process performance indices (P_p, P_pk)