Objective 1.5 Communicate with Stakeholders AIP-210 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data science team has developed a machine learning model that predicts equipment failure. The team needs to convince the plant operations manager to adopt the model. The manager is skeptical about the model's value. Which approach is most effective?

   • AGuarantee that the model will eliminate all equipment failures.
   • BExplain the technical superiority of the model compared to other algorithms.
   • CEmphasize that the model is state-of-the-art and used by leading companies.
   • DPresent a cost-benefit analysis showing reduced downtime and maintenance costs, with a pilot study on historical data.
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2. 2expert · hard

   A data science team has built a model that predicts customer lifetime value (CLV). The marketing team wants to use it to target high-value customers. However, the finance team is concerned about the model's accuracy and the cost of false positives. The model has a 90% precision rate. What is the best way to communicate the business value while addressing the finance team's concern?

   • ATell the finance team that the model is good enough and they should not worry about the details.
   • BPresent the model's precision and explain that a 10% false positive rate means some marketing spend may be wasted, but the overall ROI is positive.
   • CEmphasize the model's high accuracy and downplay the false positives.
   • DFocus only on the potential revenue increase without mentioning the false positives.
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4. 3application · medium

   A company is deploying an AI system that automates invoice processing. The finance team is worried that the system will make errors that go unnoticed. The project manager knows the system has a confidence threshold and will flag low-confidence invoices for human review. What is the best way to communicate this to the finance team?

   • AAvoid discussing the error potential to prevent worry.
   • BTell the finance team that they will need to double-check every invoice manually.
   • CAssure the finance team that the system is 100% accurate and will never make an error.
   • DExplain that the system only processes invoices it is confident about and sends uncertain ones to a human for review.
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5. 4foundation · easy

   A data science team is presenting a new recommendation engine to the company's engineering leadership. Which message best communicates the business value of the solution?

   • AThe recommendation engine is expected to increase average order value by 8%, leading to an estimated $500K in additional annual revenue
   • BThe recommendation engine will be integrated into the existing microservices architecture
   • CThe recommendation engine uses collaborative filtering and matrix factorization techniques
   • DThe recommendation engine will require additional GPU resources for training
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6. 5application · medium

   A team is developing an AI system to detect fraud in real-time transactions. The team includes data scientists, fraud analysts, and IT operations. The fraud analysts have noticed that the system is flagging too many legitimate transactions. What is the best way to address this issue collaboratively?

   • ASet up a meeting where fraud analysts can share examples of false positives and work with data scientists to adjust the model.
   • BDisable the system until the false positive rate is zero.
   • CAsk the data scientists to reduce the false positives without consulting the fraud analysts.
   • DTell the fraud analysts that the system is working as designed and they should adapt.
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