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CertNexusCertified Data Science Practitioner (CDSP)

Domain 1Objective 2

Objective 1.2 Understand Challenges CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

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.

40questions here
8free pages
11concepts
7-9%of the exam

Questions 16–20

  1. 16expert · hard

    A multinational corporation is building a global HR analytics platform that will process employee data from multiple countries, including EU member states. The data science team wants to centralize the data in a single data lake for analysis. The legal team has identified GDPR as the primary regulation for EU employee data. What is the most important governance consideration?

    Select an answer first
  2. 17application · medium

    A US healthcare provider wants to build a predictive model for patient readmission risk using electronic health records (EHR) that include patient names, diagnoses, and treatment histories. The data science team plans to store the data in a shared analytics environment. What is the primary compliance requirement the team must address?

    Select an answer first
  3. 18application · medium

    A multinational company operates in the EU, the US (including California), and other regions. It is building a global customer analytics platform that will process personal data from customers in all regions. What is the most important consideration for the data science team?

    Select an answer first
  4. 19expert · hard

    A health and fitness app company wants to use user health data (e.g., heart rate, sleep patterns) to build a personalized wellness model. The app is available in the EU and California. The company currently has a general privacy policy but no specific consent mechanism for health data. What is the most appropriate action?

    Select an answer first
  5. 20application · medium

    A data science team is considering using a new deep learning approach for image classification. They have a small labeled dataset and want to determine if the approach can achieve at least 90% accuracy before investing in more data collection and infrastructure. What should the team do?

    Select an answer first
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