A retail company wants to segment its customers for targeted marketing. They have transaction data (purchase amounts, frequency), demographic data (age, income), and website behavior data (pages viewed, time on site). They do not have predefined customer segments. Which approach is most appropriate, and what is a key consideration?

Classification using labeled customer segments from a previous campaign

Optimization to maximize marketing ROI by selecting customer groups

Clustering using a combination of numerical and categorical features, with feature scaling and encoding

Regression to predict customer lifetime value, then segment by predicted value

A bank wants to automatically approve or decline loan applications. They have historical data on applicants' income, credit score, employment status, and loan amount, along with the final decision (approved/declined) for each past application. Which modeling approach should they use?

Regression to predict the loan amount

Forecasting to predict future loan defaults

Clustering to group similar applicants

Classification to assign each new application to 'approve' or 'decline'

A hospital wants to predict whether a patient will develop diabetes within the next five years. They have structured EHR data (age, BMI, blood pressure, family history) and unstructured clinical notes. The target is a binary outcome (yes/no). Which modeling approach and data handling strategy is most appropriate?

Regression using only structured features to predict a risk score

Classification using both structured features and text features extracted from clinical notes via NLP

Clustering using both structured and unstructured data to find patient groups

Classification using only structured features, ignoring clinical notes

CertNexusCertified Data Science Practitioner (CDSP)

Domain 1Objective 3

Objective 1.3 Classify a Question into a Known Data Science Problem CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 2)

Part of the 1.0 Defining the need to be addressed through the application of data science domain, which accounts for 7-9% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

45questions here

9free pages

15concepts

7-9%of the exam

Questions 6–10

6foundation · easy
A utility company wants to predict electricity demand for the next month using historical consumption data. This is an example of which type of data science problem?
Select an answer first
7expert · hard
A retail company wants to segment its customers for targeted marketing. They have transaction data (purchase amounts, frequency), demographic data (age, income), and website behavior data (pages viewed, time on site). They do not have predefined customer segments. Which approach is most appropriate, and what is a key consideration?
Select an answer first
8foundation · easy
A manufacturer wants to determine the optimal mix of products to produce in order to maximize profit, given limited raw materials and production time. This is an example of which type of data science problem?
Select an answer first
9application · medium
A bank wants to automatically approve or decline loan applications. They have historical data on applicants' income, credit score, employment status, and loan amount, along with the final decision (approved/declined) for each past application. Which modeling approach should they use?
Select an answer first
10expert · hard
A hospital wants to predict whether a patient will develop diabetes within the next five years. They have structured EHR data (age, BMI, blood pressure, family history) and unstructured clinical notes. The target is a binary outcome (yes/no). Which modeling approach and data handling strategy is most appropriate?
Select an answer first

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Objective 1.3 Classify a Question into a Known Data Science Problem CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 2)

Questions 6–10

A utility company wants to predict electricity demand for the next month using historical consumption data. This is an example of which type of data science problem?

A manufacturer wants to determine the optimal mix of products to produce in order to maximize profit, given limited raw materials and production time. This is an example of which type of data science problem?

A bank wants to automatically approve or decline loan applications. They have historical data on applicants' income, credit score, employment status, and loan amount, along with the final decision (approved/declined) for each past application. Which modeling approach should they use?

Keep studying — 1.0 Defining the need to be addressed through the application of data science