DP-100 Question #116: Real Exam Question with Answer & Explanation

Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are creating a new experiment in Azure Machine Learning Studio. One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Stratified split for the sampling mode. Does the solution meet the goal?

Options

• AYes
• BNo

Explanation

{"question_number": 6, "correct_answer": "B - No", "explanation": "Stratified split does not compensate for class imbalance - it preserves the existing class distribution across the train/test split rather than correcting it. If the original dataset has a 95/5 class ratio, a stratified split will produce subsets with the same 95/5 ratio. To actually address class imbalance, you would use techniques such as SMOTE (Synthetic Minority Over-sampling Technique), random oversampling of the minority class, random undersampling of the majority class, or class weighting during model training. The solution does not meet the goal.", "generated_by": "claude-sonnet", "llm_judge_score": 4}

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