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A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the dat
a. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
Which of the following issues should a data scientist be most concerned about when generating a synthetic data set?
See the explanation below.
If synthetic data don't accurately mirror the real-world distributions and relationships, any models trained on them will perform poorly in deployment. Representativeness is the critical concern when generating synthetic data.
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?
See the explanation below.
Edit distance quantifies how many single-character insertions, deletions, or substitutions are needed to transform one string into another, making it a direct measure of their similarity.
A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)
See the explanation below.
One-hot encoding creates binary indicator columns for each category, allowing models to treat nominal categories without implying any order.
Label encoding maps categories to integer labels, which can be useful for tree-based models or when you need a single numeric column (though you must ensure the algorithm can handle treated ordinality appropriately).
Given these business requirements:
Which of the following is the most likely optimization technique a data scientist would apply?
See the explanation below.
You must optimize boat trips subject to strict resource limits (fuel, boat capacity, travel distance), making this a constrained optimization problem (e.g., solvable via linear programming).
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