Challenges in Data Preparation CT-AI Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   A dataset contains a few extreme values that are clearly due to sensor malfunctions. Which data cleaning technique is most appropriate to address these values?

   • AApplying one-hot encoding
   • BRemoving the outliers
   • CPerforming feature scaling
   • DSplitting the data
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What is data leakage in the context of machine learning?

   • AInformation from the test set influencing the training process
   • BThe process of encrypting data before training
   • CRemoving outliers from the dataset
   • DThe loss of data due to storage failures
   Reveal Solution Discussion Select an answer first
4. 18application · medium

   A team is preparing a dataset for a sentiment analysis model. The text data contains many typos, slang, and inconsistent capitalization (e.g., 'GREAT', 'great', 'GrEaT'). What is the most appropriate data cleaning step to address this issue?

   • ARemove all punctuation and numbers from the text.
   • BUse a bag-of-words representation without any preprocessing.
   • CApply stemming to reduce words to their root form.
   • DConvert all text to lowercase and apply spell correction.
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   Which data transformation technique converts a categorical variable with no inherent order into a set of binary columns, one for each category?

   • AOne-hot encoding
   • BMin-max scaling
   • CStandardization
   • DLog transformation
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   Which of the following is a common data quality issue that occurs when a dataset contains extreme values that are not representative of the underlying distribution?

   • AMissing values
   • BOutliers
   • CNoise
   • DInconsistencies
   Reveal Solution Discussion Select an answer first