SDS Problem identification and data readiness

Written by Saad Rahman (ACCA)

اے سی سی اے · اسٹریٹجک پروفیشنل لیول میں نیا · 7 سوالات

نام: ____________________   تاریخ: ____________

1. In a data science project, the first stage of CRISP-ML is:

   • A Monitoring
   • B Business and data understanding
   • C Deployment
   • D Modelling

2. Which statement describes a well-scoped data science problem?

   • A It starts with the choice of algorithm
   • B It has no stakeholders
   • C It states a specific decision, a measure of success and the data available
   • D It says 'use AI to improve results'

3. A data quality audit would check completeness, accuracy, consistency and:

   • A The server brand
   • B The number of users
   • C Timeliness and validity
   • D The colour of the charts

4. A model is built on five years of customer data collected only from branch customers, and will be used for online customers. The main concern is:

   • A The model is too fast
   • B The users are too many
   • C The data may not be representative
   • D The data is too clean

5. A deployed model's accuracy falls over time because customer behaviour has changed. This is called:

   • A Underfitting
   • B Data leakage
   • C Model drift
   • D Overfitting

6. Early stakeholder engagement in a data science project is important mainly because it:

   • A Reduces the need for data
   • B Allows the team to avoid documentation
   • C Ensures that the work addresses a real need and increases the chance the result is used
   • D Avoids all risk

7. Which of these is a data preparation task?

   • A Handling missing values and encoding categories
   • B Presenting results to the board
   • C Monitoring drift
   • D Agreeing the problem