Describe Best Practices for Communicating Findings and Operationalizing an Analytics Project DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 5)

Questions 21–23

1. 21foundation · easy

   A data science team has completed a predictive model for customer churn. The team needs to present the results to the marketing department, which is interested in actionable insights rather than technical details. Which approach best tailors the findings to this audience?

   • APresent a high-level summary of key drivers of churn and recommended actions
   • BShow the raw data and code used for the analysis
   • CProvide a detailed explanation of the model's algorithm and training process
   • DFocus on model accuracy metrics and feature importance rankings
   Reveal Solution Discussion Select an answer first
2. 22application · medium

   A data scientist must present a complex analysis of customer purchasing patterns to a non-technical marketing team. The goal is to convince them to launch a new targeted campaign. Which presentation approach best applies storytelling principles?

   • AStart with a clear narrative: the problem (declining repeat purchases), the insight (a specific segment responds to a different product mix), and the recommended action (targeted campaign).
   • BLead with a detailed explanation of the statistical methods used, followed by all the data tables and charts in the appendix.
   • CPresent all the data visualizations first, then let the audience draw their own conclusions from the charts.
   • DUse a dense, jargon-heavy slide deck that demonstrates the technical rigor of the analysis to build credibility.
   Reveal Solution Discussion Select an answer first
3. 23application · medium

   A logistics company is deploying a model to predict delivery delays. The model will be integrated into their existing dispatch system. Which consideration is most important to address during the deployment phase?

   • APresent the model's results to the dispatch team in a meeting to get their feedback before deployment.
   • BEnsure the model can be integrated with the dispatch system's API and can handle the expected volume of requests with low latency.
   • CEnsure the model is retrained on the most recent data right before deployment to maximize accuracy.
   • DFocus on creating a detailed report of the model's development process for the company's documentation.
   Reveal Solution Discussion Select an answer first