Objective 1.1 Identify the Project Scope CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the primary purpose of identifying project limitations during the scoping phase?

   • ATo set realistic expectations and plan for potential risks
   • BTo increase the project budget
   • CTo select the best data science algorithms
   • DTo assign tasks to team members
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2. 17expert · hard

   A financial technology company is building a model to detect fraudulent transactions. The project has a hard regulatory deadline of 3 months. The data science team has access to a large dataset, but it is stored in a data lake that requires significant preprocessing. The team also needs to ensure the model is explainable for regulatory compliance. The project manager must create a timeline. What is the best way to structure the timeline?

   • AAllocate specific time for data preprocessing, model development, explainability testing, and regulatory review, with a buffer at the end.
   • BSet a single deadline for the final model and work backward, allocating time based on the team's estimates.
   • CFocus on model development first and defer explainability testing until after the model is built.
   • DAsk the regulatory team to review the model after deployment, and assume that will be acceptable.
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4. 18foundation · easy

   What is the key difference between mandatory and optional deliverables in a data science project?

   • AMandatory deliverables are required for project success, while optional deliverables are not
   • BMandatory deliverables are more expensive to produce
   • CMandatory deliverables are always models, while optional deliverables are reports
   • DMandatory deliverables are produced by the data science team, while optional ones are produced by stakeholders
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5. 19application · medium

   A startup is building a recommendation engine for its e-commerce platform. The team has limited compute resources and a small dataset. The CEO wants a full-featured system with real-time recommendations, but the budget only allows for a batch recommendation system. Which deliverables should be considered mandatory?

   • AA real-time recommendation system with a complex infrastructure, as requested by the CEO.
   • BA batch recommendation model that can be updated daily, and a simple API to serve the recommendations.
   • CA user interface for the recommendation engine and a mobile app integration.
   • DA recommendation model and a comprehensive A/B testing framework to evaluate it.
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6. 20foundation · easy

   Which of the following is an example of a mandatory deliverable in a data science project?

   • AA deployed predictive model that meets the acceptance criteria
   • BAn optional exploratory data analysis report
   • CA supplementary visualization dashboard
   • DA nice-to-have documentation of the data cleaning process
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