Download GSDC Certified HR Analytics 3O Exam and more Exams Technology in PDF only on Docsity!
Question 1. What is the primary purpose of moving from HR scorecards to workforce analytics? A) To increase the number of metrics reported B) To replace descriptive reporting with predictive insight C) To eliminate the need for HR data collection D) To focus solely on financial KPIs Answer: B Explanation: Workforce analytics shifts the focus from merely reporting past performance (scorecards) to using data to predict future trends and inform strategic decisions.
Question 2. Which component of the LAMP framework addresses the “story behind the data”? A) Logic B) Analytics C) Measures D) Process Answer: A Explanation: “Logic” in LAMP refers to understanding the context and narrative that explains why data looks the way it does.
Question 3. In HR analytics, the distinction between reporting and analytics is best described as: A) Reporting tells “what happened”; analytics tells “why it happened.”
B) Reporting tells “why it happened”; analytics tells “what happened.” C) Both reporting and analytics only describe past events. D) Analytics replaces reporting entirely. Answer: A Explanation: Reporting answers the factual “what,” while analytics adds diagnostic or predictive layers to explain the “why.”
Question 4. Which HR data source is most appropriate for analyzing the effectiveness of learning interventions? A) ATS (Applicant Tracking System) B) LMS (Learning Management System) C) Payroll system D) Time‑and‑attendance system Answer: B Explanation: The LMS captures course completions, assessment scores, and participation, making it ideal for learning effectiveness analysis.
Question 5. When handling missing values in a turnover dataset, which technique is generally preferred for preserving statistical integrity? A) Deleting the entire record B) Replacing with the overall mean C) Using multiple imputation D) Filling with zeroes
Question 8. Survival analysis in turnover studies is primarily used to: A) Predict the exact number of resignations each month B) Estimate the probability of an employee staying beyond a certain tenure C) Calculate average salary increases over time D) Determine the most common exit interview reason Answer: B Explanation: Survival analysis models “time‑to‑event,” estimating the likelihood an employee remains with the organization past a given time point.
Question 9. A strong positive correlation between engagement scores and productivity indicates: A) Engagement causes higher productivity B) Higher productivity causes higher engagement C) A statistical association, but causality is not proven D) No relationship exists Answer: C Explanation: Correlation shows association; without experimental design, causality cannot be inferred.
Question 10. When analyzing performance distribution, a “Power Law” pattern suggests: A) Most employees cluster around the mean
B) A small number of employees account for a large share of performance outcomes C) Performance is evenly spread across the workforce D) Performance follows a normal distribution Answer: B Explanation: Power Law distributions are “heavy‑tailed,” indicating a few high performers dominate outcomes.
Question 11. Which statistical technique is commonly used for flight‑risk modeling in HR? A) Linear regression B. Decision trees C) Logistic regression D) K‑means clustering Answer: C Explanation: Logistic regression predicts binary outcomes (e.g., stay vs. leave), making it suitable for flight‑risk models.
Question 12. In succession planning, a “potential” score is best derived from: A) Tenure alone B) Past performance ratings only C) A combination of performance, leadership assessments, and readiness indicators D) Salary level Answer: C
Question 15. Markov analysis in workforce planning is primarily used to: A) Forecast external labor market trends B) Model employee movement between states (e.g., role, department) over time C) Calculate average compensation increases D) Identify skill gaps in the organization Answer: B Explanation: Markov models capture probabilities of transitions between defined states, helping predict workforce flows.
Question 16. When conducting an external labor market forecast, which data source provides the most current wage trends? A) Internal HRIS salary tables B) Bureau of Labor Statistics (BLS) occupational data C) Employee exit interview notes D) Internal performance appraisal scores Answer: B Explanation: BLS provides up‑to‑date occupational wage statistics across regions and industries.
Question 17. In a “Buy vs. Build vs. Borrow” talent strategy, “borrow” typically refers to: A) Hiring permanent full‑time employees B) Outsourcing work to external vendors or contractors
C) Training existing staff for new skills D) Purchasing software tools Answer: B Explanation: “Borrow” means leveraging contingent or contract labor to fill talent needs temporarily.
Question 18. Organizational Network Analysis (ONA) primarily measures: A) Employee satisfaction scores B) Formal reporting hierarchies only C) The informal communication and collaboration patterns among employees D. Salary equity across departments Answer: C Explanation: ONA maps informal networks, revealing who collaborates, shares information, and influences others.
Question 19. Which of the following is a key ethical concern when using AI for candidate screening? A) Reducing time‑to‑fill B) Ensuring algorithmic bias does not disadvantage protected groups C) Increasing the number of applicants D) Lowering recruitment costs Answer: B
Question 22. Which of the following best describes “Time‑to‑Hire” as a metric? A) The total cost incurred from posting a job to onboarding B) The number of days from job requisition approval to candidate acceptance C) The average tenure of new hires D) The ratio of offers accepted to offers extended Answer: B Explanation: Time‑to‑Hire measures the elapsed time from the start of the hiring process to the acceptance of an offer.
Question 23. In predictive modeling, the term “overfitting” refers to: A) A model that performs well on training data but poorly on new data B) A model that underestimates future trends C) Using too few variables in the model D) A model that is too simple to capture patterns Answer: A Explanation: Overfitting occurs when a model memorizes training data noise, reducing its generalizability.
Question 24. Which HR metric would you most likely analyze using a bell‑curve distribution? A) Employee turnover rates
B) Salary ranges across the organization C) Performance appraisal scores (when normally distributed) D) Number of open requisitions Answer: C Explanation: Performance scores often approximate a normal (bell‑curve) distribution when assessments are standardized.
Question 25. The “logic” component of LAMP is most closely aligned with which HR activity? A) Generating dashboards for senior leadership B. Conducting root‑cause analysis of high turnover C) Automating payroll calculations D) Designing employee benefit plans Answer: B Explanation: Logic involves understanding why data shows certain patterns, which is the essence of root‑cause analysis.
Question 26. Which of the following is a key advantage of integrating external labor market data with internal HRIS data? A) Eliminates the need for internal data quality checks B) Enables benchmarking of compensation and talent supply against market trends C) Guarantees higher employee engagement scores D) Reduces the cost of HR technology platforms
Question 29. In the context of HR analytics, “process” within LAMP primarily focuses on: A) Data visualization techniques B) Implementing actions based on analytical insights C) Collecting raw employee data D) Defining key performance indicators Answer: B Explanation: Process translates insights into concrete changes, such as policy updates or interventions.
Question 30. Which of the following best illustrates a prescriptive analytics output? A) A line chart showing monthly attrition trends B) A regression equation predicting future turnover C) A recommendation to increase flexible work options to reduce projected turnover by 5% D) A heat map of employee satisfaction scores Answer: C Explanation: Prescriptive analytics provides actionable recommendations based on predictive insights.
Question 31. When assessing the ROI of an HR initiative, which of the following is essential to include?
A) Only the upfront investment cost B) Both the cost of the initiative and the quantifiable financial benefit it generates C) The number of HR staff involved D) The length of the project timeline Answer: B Explanation: ROI calculation requires both costs incurred and measurable benefits (e.g., reduced turnover cost).
Question 32. A “high‑potential” employee identification model typically uses which type of variable as a predictor? A) Age only B) Tenure length exclusively C) Composite scores combining performance, leadership assessments, and learning agility D. Salary grade Answer: C Explanation: High‑potential models blend multiple dimensions to capture future leadership capability.
Question 33. Which data privacy principle requires that personal data be collected only for a specific, explicit purpose? A) Data minimization B) Purpose limitation C) Accuracy
Explanation: Difficulty filling critical roles despite a large applicant pool signals a mismatch between required and available skills.
Question 36. When performing a correlation analysis between compensation and performance, a correlation coefficient of 0.05 indicates: A) Strong positive relationship B) Strong negative relationship C) No meaningful linear relationship D) Perfect linear relationship Answer: C Explanation: A coefficient near zero suggests little to no linear association.
Question 37. Which HR metric is most directly linked to measuring “employee productivity”? A) Absenteeism rate B) Revenue per employee C) Time‑to‑Hire D) Turnover rate Answer: B Explanation: Revenue per employee quantifies output relative to headcount, a common productivity indicator.
Question 38. In a logistic regression model for flight risk, the odds ratio for “Employee tenure < 1 year” is 2.5. This means: A) Employees with <1 year tenure are 2.5 times more likely to leave than longer‑tenured employees, holding other factors constant. B) Tenure has no impact on flight risk. C) Tenure reduces the probability of leaving by 2.5%. D) The model is incorrectly specified. Answer: A Explanation: An odds ratio >1 indicates increased odds of the event (resignation) for the specified group.
Question 39. Which of the following best describes “right‑size” workforce planning? A) Hiring as many employees as possible regardless of need B) Aligning workforce size with strategic business objectives and market demand C) Maintaining a constant headcount over time D) Outsourcing all non‑core functions Answer: B Explanation: Right‑sizing ensures the workforce matches current and future strategic requirements.
Question 40. When using K‑means clustering to segment employees for benefits, the appropriate number of clusters should be determined by:
Answer: B Explanation: Time‑lagged regression can test whether certain variables predict future disengagement, identifying leading indicators.
Question 43. In the context of HR data security, “encryption at rest” refers to: A) Protecting data while it is being transmitted over a network B) Encrypting data stored on servers, databases, or backups C) Using passwords for system login D) Deleting data after use Answer: B Explanation: Encryption at rest secures stored data, preventing unauthorized access if storage devices are compromised.
Question 44. When evaluating the impact of a new mentorship program on promotion rates, the appropriate experimental design is: A) Cross‑sectional survey B) Pre‑post quasi‑experimental study with a control group C) Time‑series analysis without a control D) Simple descriptive reporting Answer: B Explanation: A pre‑post design with a comparable control group isolates the program’s effect on promotions.
Question 45. Which of the following is a direct output of a “headcount forecasting” model? A) List of employees to be terminated B) Projected number of employees needed by function for each future quarter C) Average salary increase percentage D) Employee satisfaction index Answer: B Explanation: Headcount forecasting predicts future staffing levels by function or department over time.
Question 46. A high “offer acceptance rate” (>90%) most likely indicates: A) Ineffective recruitment sourcing B) Competitive compensation and strong employer brand C) Long time‑to‑hire D) Poor candidate experience Answer: B Explanation: A high acceptance rate suggests candidates find the offer attractive and the employer appealing.
Question 47. Which of the following best illustrates “bias mitigation” in AI‑driven hiring? A) Removing all demographic fields from the dataset before model training B) Using a larger dataset without any preprocessing
