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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define data accuracy and why it is important in AI.
💡 Hint: Think about how incorrect data could mislead AI outcomes.
Question 2
Easy
What does data completeness mean?
💡 Hint: Consider what would happen if you don’t have all the data needed.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
Which of the following factors does NOT contribute to data quality?
- Accuracy
- Timeliness
- Creativity
💡 Hint: Think about different attributes that a dataset can have.
Question 2
True or False: Completeness is about ensuring all necessary data is available.
- True
- False
💡 Hint: Consider what could happen if data is incomplete.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have two datasets: one is complete but outdated, and the other is accurate but missing some entries.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💡 Hint: Consider the implications of using outdated data.
Question 2
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.
Challenge and get performance evaluation