7.2.4 - Data Quality Considerations
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
Define data accuracy and why it is important in AI.
💡 Hint: Think about how incorrect data could mislead AI outcomes.
What does data completeness mean?
💡 Hint: Consider what would happen if you don’t have all the data needed.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
Which of the following factors does NOT contribute to data quality?
💡 Hint: Think about different attributes that a dataset can have.
True or False: Completeness is about ensuring all necessary data is available.
💡 Hint: Consider what could happen if data is incomplete.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have two datasets: one is complete but outdated, and the other is accurate but missing some entries. Which dataset do you prioritize for an AI project and why?
💡 Hint: Consider the implications of using outdated data.
Identify possible biases that could arise in an AI model trained on data from a specific demographic group. What solutions can you propose to mitigate these biases?
💡 Hint: Think about representation and inclusivity in datasets.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.