DP-100 · Question #71
DP-100 Question #71: Real Exam Question with Answer & Explanation
The correct answer is B: No. {"question_number": 7, "correct_answer": "B. No", "explanation": "Last Observation Carried Forward (LOCF) is a time-series imputation technique that fills missing values by propagating the last known observed value forward. While it preserves dimensionality, it is only meaningful
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 analyzing a numerical dataset which contain missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points. Does the solution meet the goal?
Options
- AYes
- BNo
Explanation
{"question_number": 7, "correct_answer": "B. No", "explanation": "Last Observation Carried Forward (LOCF) is a time-series imputation technique that fills missing values by propagating the last known observed value forward. While it preserves dimensionality, it is only meaningful for ordered/sequential (time-series) data where observations are temporally related. For a general numerical dataset that is not time-ordered, LOCF is inappropriate and can introduce bias. The correct approach for imputing missing values in a non-time-series numerical dataset without reducing dimensionality would be mean, median, or mode substitution.", "generated_by": "claude-sonnet", "llm_judge_score": 4}
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