Pass the ASQ ASQ certification CQE Questions and answers with CertsForce

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.

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.

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.

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.

Fault tree analysis (FTA) is a tool that begins at the point of failure and works back through the process to the components. FTA is used to identify the root causes of system failures by constructing a logical diagram of the various possible causes. This method helps in understanding the paths leading to a failure and identifying corrective actions. Reference: "Reliability, Maintainability, and Risk" by David J. Smith, which describes fault tree analysis and its application in failure analysis.

In Failure Mode and Effects Analysis (FMEA), the focus should be on implementing corrective actions on items that have high Risk Priority Numbers (RPN) or high severity values. The RPN is calculated by multiplying the severity, occurrence, and detection ratings, and it helps prioritize which failure modes need immediate attention.

When measurements are taken using a reference scale, such as a pair of calipers, the resulting data are referred to as continuous data. Continuous data can take any value within a given range and are measured on a scale that can be subdivided. This type of data is crucial for precise quality control and statistical analysis in manufacturing and engineering.