Model Building - Describe Details of This Phase, Including Activities and Associated Roles DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 4)

Questions 16–20

1. 16application · easy

   A retail analytics team is following the data analytics lifecycle. They have just finished preparing the data and are about to start building a recommendation model. Which statement best describes the position of the model building phase in the lifecycle?

   • AIt is the first phase of the lifecycle, before data discovery
   • BIt is an optional phase that can be skipped if data is clean
   • CIt occurs after data preparation and before the operationalize phase
   • DIt occurs after operationalize and before data preparation
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   During which stage of the data analytics lifecycle does the team typically produce the first version of a predictive model?

   • ACommunicating results
   • BData discovery
   • CModel building
   • DData preparation
   Reveal Solution Discussion Select an answer first
4. 18application · medium

   A logistics company is building a model to predict delivery delays. The data scientist has trained a model and now needs to assess its performance. Which metric is most appropriate to evaluate a binary classification model?

   • APerplexity and BLEU score
   • BMean squared error and R-squared
   • CSilhouette score and Davies-Bouldin index
   • DAccuracy, precision, recall, and F1-score
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   In the data analytics lifecycle, which statement best describes the position and purpose of the model building phase?

   • AIt is a middle phase where the team selects and trains algorithms on prepared data to create a predictive model.
   • BIt is an early phase focused on collecting and cleaning raw data from various sources.
   • CIt is an optional phase that only occurs if the data quality is poor and requires additional cleaning.
   • DIt is the final phase where the model is deployed into production and monitored for performance.
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   An e-commerce company is building a product recommendation model. The data scientist has trained a collaborative filtering model, but the model's performance is poor. The team needs to improve the model. Which action should the data scientist take?

   • ATry a different algorithm, such as matrix factorization, and compare performance
   • BAsk the data engineer to add more servers to the cluster
   • CDeploy the model anyway and monitor its performance in production
   • DReduce the amount of training data to simplify the model
   Reveal Solution Discussion Select an answer first