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Knowledge check

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Test Your Knowledge

Evaluate your understanding of technical documentation for ML projects by completing this knowledge check. This will help you identify areas where you may need to review the material.

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You join a team working on an ML fraud detection system. The repository contains only code files with no README, data dictionary or notes. What's the biggest risk for the team?

Which example best demonstrates documentation that supports the work of other team members?

Your head of product asks for documentation on a churn prediction model. What should you prioritise?

You present the same documentation to both your head of product and a junior data scientist. One feels overwhelmed by jargon; the other says the content is too vague. What principle did you fail to apply?

Your team has built an ML model to predict equipment failures in a manufacturing plant. You need to document it for three groups — executives, compliance officers and engineers. Which approach best demonstrates tailoring documentation with a layered approach?

A team dropped a variable due to multicollinearity but never recorded the decision. Months later, new hires reintroduced it, causing errors. What maintenance practice would have prevented this?

Which of the following best reflects 'documentation as code'?

During a sprint review, you discover documentation describing an old model version that's no longer in production. What's the best corrective action?

You're asked why your team uses model cards instead of freeform reports. What's the best justification?

During a sprint review, a teammate argues that writing documentation is unnecessary because 'the code already explains everything'. What's the strongest counterargument?

Action item: Knowledge check

Complete the assessment below to verify your mastery of this unit's concepts.

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