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Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

Uncertainty, Calibration and Abstention Thresholds

Quiz: Uncertainty, Calibration and Abstention Thresholds

Explainability Methods and Their Honest Limits

Quiz: Explainability Methods and Their Honest Limits

Validation by Someone Who Did Not Build the Model

Quiz: Validation by Someone Who Did Not Build the Model

Reproducibility: Seeds, Environments and Experiment Records

Quiz: Reproducibility: Seeds, Environments and Experiment Records

Writing the Model Card and the Technical File

Quiz: Writing the Model Card and the Technical File

Handover to Monitoring: What Production Needs From You

Quiz: Handover to Monitoring: What Production Needs From You

Final exam: Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

2 of 2

Final exam: Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Final exam: Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

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Question 1 of 10

1. Question

When did EU AI Act Article 5 prohibitions come into force?

1 January 2025
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2 February 2025
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2 August 2026
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1 October 2026
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Question 2 of 10

2. Question

What is intended purpose in the context of regulated AI?

The highest accuracy a model can theoretically achieve
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The specific task, population and context the model is designed for
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The performance metrics the model achieved on test data
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The business benefit the organisation expects
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Question 3 of 10

3. Question

What is dataset provenance?

The accuracy of a dataset on held-out test data
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Knowledge of where a dataset came from, who collected it and for what purpose
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The size and number of records in a dataset
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Whether the dataset is stored in a database or a spreadsheet
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Incorrect / 1 Points
Question 4 of 10

4. Question

What is representativeness in a training dataset?

The dataset has no missing values
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The dataset reflects the distribution of the population the model will serve
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The dataset has equal numbers of all classes
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The dataset is stored in a standard format
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Question 5 of 10

5. Question

What is special category data under GDPR?

Any data stored in a database
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Data revealing race, health, religion or trade union membership
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Data that is more than 5 years old
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Data used to train machine learning models
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Question 6 of 10

6. Question

What is the gold standard approach to splitting data?

Random split 50/50
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Temporal split: train on past, test on future
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Stratified split to match class distributions
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No split: train and test on the same data
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Question 7 of 10

7. Question

Why is accuracy sometimes a misleading metric?

It requires too much computation
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In imbalanced data, accuracy can be high by predicting the majority class always
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Accuracy only works for binary classification
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Accuracy requires a validation set
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Question 8 of 10

8. Question

Why cannot all fairness metrics be satisfied at once?

Because fairness is subjective
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Because of impossibility of equalization: if groups have different base rates, you cannot simultaneously achieve equal accuracy, equal positive rate and equal false positive rate
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Because models are inherently biased
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Because fairness only applies to high-risk decisions
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Question 9 of 10

9. Question

What is subgroup analysis?

Analysing subsets of a single subgroup
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Calculating performance metrics separately for different population subgroups
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Removing underperforming data
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Training separate models for each subgroup
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Question 10 of 10

10. Question

What is calibration?

The process of training a model
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When predicted probabilities match actual probability of positive outcomes
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Adjusting model accuracy
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Splitting data for validation
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Correct / 1 Points

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