Data Quality and Its Effect on the ML Model CT-AI Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which of the following is a key dimension of data quality in machine learning?

   • AThroughput
   • BScalability
   • CCompleteness
   • DLatency
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2. 12foundation · easy

   Which of the following is a common data quality issue in ML datasets?

   • AUnderfitting
   • BHigh variance
   • CMissing values
   • DOverfitting
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4. 13foundation · easy

   Which technique is commonly used to handle missing values in a dataset?

   • ANormalization
   • BRegularization
   • CImputation
   • DFeature selection
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5. 14foundation · easy

   What is a 'label error' in the context of supervised ML data quality?

   • AAn incorrect target value assigned to a training example
   • BA duplicate record in the dataset
   • CA missing feature value in the input data
   • DAn outlier in the feature space
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6. 15application · medium

   A logistics company is training a model to predict delivery delays. The dataset includes a 'package_weight' column. A data analyst notices that 2% of the records have weights of 500 kg, while the maximum legitimate weight for the company's fleet is 50 kg. The analyst suspects these are data-entry errors. What is the MOST appropriate first step?

   • ARemove the 500 kg records immediately to prevent them from skewing the model.
   • BReplace the 500 kg values with the mean weight of the dataset.
   • CInvestigate the source of the 500 kg values to determine whether they are true outliers or systematic errors.
   • DCap all weights above 50 kg at 50 kg to keep the records in the dataset.
   Reveal Solution Discussion Select an answer first