Objective 1.1 Identify the Project Scope CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A retail company wants to build a churn prediction model to reduce customer attrition. The business sponsor states that the model must be deployed by the end of the quarter to meet a regulatory deadline, but the data engineering team has not yet consolidated the required customer transaction data. The data science team is asked to define the project specifications. Which approach best aligns the project scope with stakeholder expectations?

   • ASet the deployment date as fixed and instruct the data engineering team to deliver the data by that date, regardless of current progress.
   • BPostpone all project planning until the data is fully available, then define objectives and metrics.
   • CDefine the model's success metric as a 10% reduction in churn, and set a milestone to review data availability before committing to the deployment date.
   • DDefine the success metric as model accuracy and assume the data will be ready in time because the sponsor requested it.
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2. 12application · medium

   A government agency is building a model to predict traffic congestion. The project has a fixed deadline of 3 months because the model must be used in an upcoming city planning review. The team has identified that the required traffic sensor data is only available from a third-party vendor, and the vendor has a history of delayed deliveries. What should the project manager do to manage the timeline effectively?

   • ASet a milestone for the vendor data delivery at the end of month 1, and plan a contingency to use alternative data sources if the delivery is late.
   • BExtend the project deadline by 1 month to accommodate the vendor's potential delay.
   • CNegotiate with the vendor to guarantee on-time delivery, and assume the rest of the project can proceed as planned.
   • DStart the project without the vendor data and use placeholder data for initial model development.
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4. 13expert · hard

   A healthcare analytics company is building a model to predict patient no-shows. The project has a strict deadline of 6 weeks. The team has access to historical appointment data, but it is stored in a legacy system that requires manual export, which takes 2 weeks. The team also needs to comply with data privacy regulations, which require de-identification of patient data. The project sponsor wants the model to be deployed in the clinic's scheduling system. Which combination of limitations is most critical to address in the project plan?

   • AThe deadline and the sponsor's expectations, as they are conflicting.
   • BThe deployment integration and the model accuracy, as they are the final deliverables.
   • CThe manual data export time and the de-identification requirement, as they both add time and complexity to the data preparation phase.
   • DThe legacy system and the clinic's scheduling system, as they are both outdated.
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5. 14expert · hard

   A transportation company is building a model to predict delivery delays. The project has a fixed deadline of 2 months. The team has identified that the required weather data is available from a public API, but the API has rate limits that could slow down data collection. The team also needs to integrate the model with the company's routing system, which is managed by another team. The project manager must create a timeline. What is the best approach?

   • APrioritize model development and delay integration until after the deadline, if necessary.
   • BCreate a timeline with a milestone for API data collection, a milestone for model integration, and a buffer for unexpected delays.
   • CAsk the routing team to provide a dedicated resource to the project, and assume that will eliminate integration delays.
   • DSet a single deadline for the final model and work backward, assuming the API and routing team will be available as needed.
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6. 15foundation · easy

   Which of the following is an example of a technical constraint that could limit a data science project?

   • AThe project budget is insufficient for additional cloud resources
   • BThe existing hardware cannot support the required model training time
   • CThe team lacks experience with deep learning
   • DThe available data contains many missing values
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