Question 7A data analyst at a marketing company determines which advertising campaign will be most successful. Rather than only reviewing data about effective past campaigns, the analyst considers target audience demographics, market trends, competitor campaigns, and more. This enables them to achieve more insightful results. Which fairness best practice does this scenario describe?1 pointOversamplingConsidering all available dataIncluding self-reported dataGuiding business strategy

Question 3Which fairness best practice is intended to help data teams better understand the context surrounding their data analysis conclusions?1 pointUse oversamplingInclude self-reported dataIdentify surrounding factorsConsider relevant data

Question 6A data professional at a grocery store considers fairness when collecting data. Rather than having store associates share observations, they create a survey that asks customers to provide information about their own shopping experiences. This helps avoid any unconscious bias that might be introduced by the sales associates. Which fairness best practice does this scenario describe?1 pointOversamplingUsing all available dataConsidering contextSelf-reporting

Question 6Data analysts ensure their analysis is fair for what reason?1 pointFairness helps them avoid biased conclusions.Fairness helps them pick and choose which data to include from a dataset. Fairness helps them communicate with stakeholders.Fairness helps them stay organized.

Question 1Which of the following statements accurately describe fairness considerations in data analysis? Select all that apply.1 pointBest practices for fairness in data analysis include identifying surrounding factors.Fairness practices should begin during the analyze phase of the data analysis process.Effective data analysts help create systems that are fair and inclusive to everyone. A data professional may include self-reported data when prioritizing fairness.

Question 6Which of the following are examples of fairness in data analysis? Select all that apply. 1 pointFactoring in social contexts that could create bias in conclusionsConsidering systematic factors that may influence dataMaking sure a sample population represents all groupsPicking and choosing which data to include from a dataset