Describe Best Practices for Building Project Presentations for Specific Audiences DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 4)

Questions 16–20

1. 16expert · hard

   A data science team has developed a model to predict supply chain disruptions. They must present the model to the company's operations director, who is responsible for mitigating risks. The director is analytical but not data science literate. The team wants to ensure the director understands the model's value and how to act on it. Which presentation approach is most effective?

   • AFocus on the model's limitations and potential biases, and avoid making specific recommendations.
   • BPresent a comprehensive list of all possible disruptions and the model's predicted probability for each, without recommendations.
   • CFrame the presentation around a recent disruption that the model would have predicted, then show how the model can be used to proactively mitigate future risks, ending with a clear call to action.
   • DStart with the model's technical specifications, such as the algorithm and feature set, then discuss its accuracy metrics.
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2. 17application · medium

   A data science team is presenting a new pricing optimization model to the finance department. The finance team is detail-oriented and wants to understand the assumptions and limitations before approving implementation. Which presentation content best matches this audience?

   • AFocus on the algorithm's implementation details, such as the programming language and libraries used.
   • BPresent a single slide with the final recommended prices and no supporting analysis.
   • CInclude a clear explanation of the model's assumptions, validation results, and sensitivity analysis, with a focus on financial impact.
   • DProvide a high-level overview of the model's benefits with minimal technical detail, focusing on the expected revenue increase.
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4. 18application · medium

   A data analyst is presenting a complex analysis of customer feedback to a group of non-technical stakeholders. The analyst notices the audience is losing attention during a detailed explanation of the methodology. What should the analyst do to re-engage the audience?

   • ASkip the methodology entirely and jump straight to the results without any context.
   • BContinue speaking faster to get through the methodology quickly and move on to the results.
   • CPause and ask the audience if they have questions or if they would like to see a real example of the feedback analysis.
   • DLower the volume of the voice to encourage the audience to listen more carefully.
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5. 19application · medium

   A data scientist is preparing a presentation for a mixed audience of business stakeholders and junior analysts. The goal is to show how customer satisfaction scores have changed over the past year and what factors drove the change. Which visual design choice best supports clarity and understanding for this audience?

   • ADisplay a complex scatter plot with regression lines for every variable in the dataset.
   • BUse a simple line chart for the trend and a bar chart for the top contributing factors, with clear labels and a brief annotation on the key inflection point.
   • CUse a 3D pie chart to show the proportion of each factor, with many colors and no labels.
   • DShow a table with all monthly satisfaction scores and factor percentages for the entire year.
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6. 20application · medium

   A data team has built a model to predict equipment failures and must present the results to plant managers who will decide on maintenance schedules. The managers are results-oriented and have limited time. Which narrative structure best supports their decision-making?

   • ABegin with a detailed history of the data collection process, then explain the model's algorithm, and finally show the results.
   • BPresent all possible model outcomes and let the managers draw their own conclusions without a specific recommendation.
   • CStart with the problem (unplanned downtime), present the model's prediction accuracy in business terms (reduced downtime), and end with a clear recommendation for the maintenance schedule.
   • DStart with the technical performance metrics, then discuss the model's limitations, and end with future research ideas.
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