Objective 3.2 Preprocess Data CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   What does MCAR stand for in the context of missing data?

   • AMissing Correlated At Random
   • BMissing Conditionally At Random
   • CMissing Completely And Randomly
   • DMissing Completely At Random
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2. 2foundation · easy

   Which dedicated function in pandas is used to identify missing values in a DataFrame?

   • Adf.fillna()
   • Bdf.replace()
   • Cdf.dropna()
   • Ddf.isnull()
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4. 3application · medium

   A data scientist is working with a dataset that contains a feature 'transaction_amount' with many extreme outliers. The data scientist wants to scale this feature for a distance-based algorithm (k-nearest neighbors) without letting the outliers dominate the scaling. What should the data scientist do?

   • AApply robust scaling using the median and interquartile range.
   • BApply min-max scaling.
   • CApply log transformation followed by min-max scaling.
   • DApply standardization (z-score).
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5. 4foundation · easy

   What is the purpose of min-max normalization?

   • ATo center data around zero with unit variance
   • BTo make the data distribution normal
   • CTo remove outliers from the data
   • DTo scale features to a specific range, typically [0, 1]
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6. 5expert · hard

   A data scientist is analyzing a clinical trial dataset. The variable 'blood_pressure' has missing values. The missingness is related to the unobserved health status of patients: sicker patients are more likely to have missing blood pressure readings. The data scientist needs to decide how to handle the missing values to minimize bias in a model predicting patient outcomes. What is the most appropriate approach?

   • AImpute using a model that includes observed covariates related to health status.
   • BImpute with the median blood pressure.
   • CDelete rows with missing blood pressure.
   • DImpute with the mean blood pressure.
   Reveal Solution Discussion Select an answer first