Describe the Data Analytics Lifecycle Purpose and Sequence of Phases DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 1)

Questions 1–5

1. 1expert · hard

   A telecommunications company is in the data preparation phase of a churn prediction project. The team discovers that the customer data contains significant missing values and inconsistent formats across different regions. They are considering whether to spend more time on data preparation or proceed to model planning. What is the most important consideration?

   • AModel planning can proceed in parallel to save time, even if data is not fully prepared.
   • BThe team should skip data preparation and rely on the model building phase to clean the data.
   • CThe data preparation phase must be completed thoroughly to ensure data quality for modeling.
   • DData preparation is optional if the team uses a robust algorithm that handles missing values.
   Reveal Solution Discussion Select an answer first
2. 2application · medium

   A marketing analytics team is using the data analytics lifecycle for a customer segmentation project. They have completed the data preparation phase and are now deciding which clustering algorithm to use. According to the lifecycle, which phase are they entering?

   • AOperationalization
   • BCommunicating results
   • CModel planning
   • DModel building
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   Which of the following lists the phases of the data analytics lifecycle in the correct order?

   • ADiscovery, Model Planning, Data Preparation, Model Building, Operationalize, Communicate Results
   • BModel Planning, Discovery, Data Preparation, Model Building, Communicate Results, Operationalize
   • CDiscovery, Data Preparation, Model Planning, Model Building, Communicate Results, Operationalize
   • DData Preparation, Discovery, Model Building, Model Planning, Operationalize, Communicate Results
   Reveal Solution Discussion Select an answer first
5. 4application · medium

   A telecom company is in the data preparation phase of an analytics project to predict customer churn. The team is merging data from multiple sources and handling missing values. What is the primary objective of this phase?

   • ATo select the appropriate predictive modeling technique.
   • BTo define the business problem and success metrics.
   • CTo clean, transform, and format the data for analysis.
   • DTo deploy the churn model into the production environment.
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   An insurance company is following the data analytics lifecycle for a project to improve claim processing. The team has just completed the model building phase and has evaluated the model's performance. What is the next phase they should enter?

   • AData preparation
   • BCommunicating results
   • CDiscovery
   • DModel planning
   Reveal Solution Discussion Select an answer first