Practice Data Quality Considerations (7.2.4) - AI Project Cycle

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10 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define data accuracy and why it is important in AI.
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Think about how incorrect data could mislead AI outcomes.
Question 2.
What does data completeness mean?
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Consider what would happen if you don’t have all the data needed.
3Question 3.
Which of the following factors does NOT contribute to data quality?
- Accuracy
- Timeliness
- Creativity
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Think about different attributes that a dataset can have.
4Question 4.
True or False: Completeness is about ensuring all necessary data is available.
- True
- False
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Consider what could happen if data is incomplete.
Question 5.
You have two datasets: one is complete but outdated, and the other is accurate but missing some entries.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?
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Consider the implications of using outdated data.
Question 6.
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?
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Think about representation and inclusivity in datasets.

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