WGU VPC2Data-Driven Decision MakingC207 Data-Driven-Decision-Making Exam Dumps: Updated Questions & Answers (January 2026)

Thebalanced scorecard (BSC)is a performance management framework that simultaneously accounts forfinancial, customer, internal process, and learning and growth metrics. In data-driven decision making, the balanced scorecard provides a holistic view of organizational performance rather than focusing on a single dimension of success.

Financial metrics assess profitability and sustainability, customer metrics evaluate satisfaction and loyalty, internal process metrics examine operational efficiency, and learning and growth metrics focus on employee development and innovation. By integrating these perspectives, the balanced scorecard ensures alignment between day-to-day operations and long-term strategic goals.

Customer complaint reports, income statements, and balance sheets each address only one aspect of performance. They do not provide the multi-dimensional insight necessary for strategic decision-making.

Therefore, the correct answer isA, balanced scorecard.

The primary problem with this survey issystematic error, which occurs when the data collection process consistently favors certain outcomes due to flawed design. In data-driven decision making, systematic error arises when a sampling method introduces bias that skews results in a predictable direction.

In this scenario, surveying only people living in California creates a location-based bias. Respondents are far more likely to prefer Amusement Park W because it is geographically closer, more familiar, and more accessible than Amusement Park X in Texas. This bias does not occur randomly; instead, it systematically influences responses toward one option, making the results unreliable for comparing overall preferences between the two parks.

Random error would involve unpredictable variation, which is not the issue here. Measurement bias relates to how questions are asked or measured, and information bias concerns inaccurate or misleading data reporting. The core issue is thenon-representative sample, which violates the principle of unbiased data collection.

Data-driven decision making emphasizes that valid conclusions require representative samples. Because the survey design inherently favors one outcome, the results cannot be generalized, makingsystematic errorthe correct answer.

Ablind studyis specifically designed to eliminate bias that occurs when participants are aware of treatment assignments. In data-driven decision making and experimental research, patient awareness of receiving a placebo or treatment can influence reported symptoms, perceived effectiveness, and behavior, thereby biasing results.

In a blind study, participants do not know whether they are receiving the treatment or the placebo. This prevents expectations or beliefs from influencing outcomes and ensures that observed effects are attributable to the treatment itself rather than psychological or behavioral factors.

Observational studies and prospective cohort studies do not involve controlled assignment of treatments and therefore cannot eliminate this type of bias. Time series studies analyze data over time but do not address participant awareness of treatment allocation.

By preventing patients from knowing their treatment group, blind studies improve internal validity and support more accurate causal inference. Therefore, the correct answer isD, blind study.

To make a valid comparison in data-driven decision making, samples must becomparable and drawn from the same population, differing only in the factor being evaluated. In this case, the goal is to compare patient satisfaction between online telemedicine visits and in-person visits atFamily Practice A.

Usingonline Family Practice A telemedicine visits and in-person Family Practice A visitsensures that both samples come from the same organization, patient base, and survey methodology. This controls for external factors such as practice standards, demographics, and survey design, allowing differences in ratings to be attributed to the visit type rather than unrelated variables.

Comparing total visits to only one visit type introduces imbalance. Including other family practices introduces external variation and invalidates the comparison. Data-driven decision making stresses consistency and relevance in sample selection to ensure accurate conclusions.

Therefore, the correct answer isD, as it uses comparable samples that isolate the variable of interest.

TheCheckphase of the Plan-Do-Check-Act (PDCA) cycle involves evaluating outcomes and analyzing results. In data-driven decision making, this step compares actual performance against expected results to determine whether objectives were met.

During the Check phase, organizations review data, assess variation, and identify opportunities for improvement. Planning defines objectives, Doing implements changes, and Acting standardizes or adjusts processes based on evaluation.

Therefore, the correct answer isA, Check.

Az-scorestandardizes 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 astandard normal distributionwith amean 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 isA, reflecting the core assumption underlying z-score usage.

In regression analysis, theindependent variableis the predictor used to explain or estimate changes in another variable. In data-driven decision making, identifying the correct variable roles is essential for meaningful interpretation.

In this scenario, land availability is used to predict commute time. Commute time is the outcome being explained, making it the dependent or target variable. Land availability influences or explains changes in commute time, which makes it theindependent variable.

Controls are additional variables included to isolate effects, but land availability is the primary predictor of interest. Therefore, optionCis correct.

Key performance indicators (KPIs) are designed to measure progress toward strategic objectives. In data-driven decision making, effective KPIs arestable, meaningful, and aligned with organizational goalsrather than frequently changing.

One important quality of KPIs is that theycan be used for internal benchmarking, allowing organizations to compare performance across departments, time periods, or projects. This helps identify best practices and performance gaps.

Another defining characteristic is that KPIs often followSMART criteria—Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria ensure KPIs are clearly defined and actionable.

KPIs are not meant to be easily changed, as frequent changes undermine consistency and comparability. They also require ongoing monitoring and maintenance to remain relevant and accurate.

Therefore, the correct answers areB and D.

Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.

When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.

While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.

Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.

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Patient-to-staff ratiosare a critical analytic measure for determining whether a hospital is adequately staffed for each shift. In data-driven decision making, staffing adequacy is best assessed by examining workload demand relative to available personnel.

Patient-to-staff ratios directly reflect how many patients each staff member is responsible for during a given shift. High ratios may indicate understaffing, increased risk of burnout, and reduced quality of care, while lower ratios suggest more manageable workloads and better patient outcomes.

Staff productivity levels measure efficiency but do not directly capture demand. Education levels reflect qualifications rather than staffing sufficiency. Patient satisfaction is an outcome metric and may be influenced by many factors beyond staffing levels.

By analyzing patient-to-staff ratios across shifts, hospital administrators can identify imbalances, allocate resources more effectively, and improve operational efficiency. Therefore, the correct answer isB.