WGU Data-Driven-Decision-Making Exam Dumps

A z-score standardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to a standard normal distribution with a mean of zero and a standard deviation of one.

This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.

Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.

Therefore, the correct answer is A, reflecting the core assumption underlying z-score usage.

The interval from 85 to 115 is exactly one standard deviation below and above the mean, since the mean is 100 and the standard deviation is 15. In a normal distribution, the empirical rule states that approximately 68 percent of observations fall within one standard deviation of the mean, about 95 percent fall within two standard deviations, and about 99.7 percent fall within three standard deviations. Because the range 85 to 115 corresponds to mean 1 standard deviation, the probability of selecting a score in that range is about 68 percent. Among the available options, 68.8 percent is the correct choice and best represents this probability. The other values correspond to wider intervals: 95.4 percent is associated with two standard deviations and 99.7 percent with three. A value of 100 percent would imply every possible score lies in that range, which is not true for a normal distribution. Therefore, the correct answer is 68.8 percent because the question describes the one-standard-deviation interval around the mean.

Analytics is classified as descriptive, predictive, or prescriptive based on the purpose of the analysis and the methods used to carry it out, which is a foundational concept in data-driven decision making. The distinction reflects the type of managerial question being addressed rather than technical aspects such as software tools, sample size, or data reliability.

Descriptive analytics focuses on understanding what has happened by summarizing historical data. It relies on descriptive statistics, reports, dashboards, and data visualizations to provide insights into past performance. Predictive analytics extends this approach to determine what is likely to happen by using statistical models, probability distributions, regression analysis, and forecasting techniques to estimate future outcomes. Prescriptive analytics goes further by identifying what should be done to achieve desired results. It uses optimization models, decision trees, simulations, and scenario analysis to recommend the best course of action under given constraints.

In data-driven decision making, the classification of analytics depends on how results are intended to support decisions and the analytical techniques applied to achieve that goal. Factors such as data quality and software influence accuracy and efficiency but do not define the analytics category itself. Therefore, the correct classification criterion is the purpose and methods, making option C the correct answer.

A cause-and-effect diagram, also known as an Ishikawa diagram or fishbone diagram, is used to brainstorm and organize possible root causes of a problem. It helps teams think systematically about why a problem may be occurring by grouping causes into logical categories such as methods, materials, people, equipment, and environment. In the scenario given, an intermittency problem suggests an issue that happens irregularly and may be difficult to isolate, which makes a cause-and-effect diagram especially useful. Its purpose is not to assign blame but to support structured problem-solving and root cause analysis. It is also not a financial risk tool or an automated software diagram generator. In quality management and data-driven improvement, this diagram encourages teams to explore multiple contributing factors before deciding on corrective action. Therefore, the correct answer is that it is used to brainstorm possible root causes for an intermittency problem.

The bakery owner wants to determine the sales level at which profit equals zero. This is the definition of break-even analysis. Break-even analysis identifies the number of units that must be sold so that total revenue exactly equals total cost, meaning there is neither profit nor loss. It is a widely used prescriptive and managerial decision tool for pricing, budgeting, production planning, and cost control. ANOVA is used to compare means across groups, not to find a zero-profit sales level. A t-test compares means between two groups, which is also unrelated to the goal of determining the required sales quantity for no profit or loss. ''Crossover'' is not the standard term for this type of profitability calculation in business analytics. Break-even analysis helps managers understand fixed costs, variable costs, contribution margin, and the minimum output required to sustain operations. Therefore, the correct method for determining how many cakes must be sold so that monthly profit equals zero is break-even analysis.