Data Wrangling DY0-001 Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which of the following is an example of ground truth labeling?

   • AMerging two datasets on a common key
   • BRemoving rows with missing values
   • CScaling numeric features to a range of 0 to 1
   • DAnnotating images with bounding boxes to indicate the location of objects
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2. 2application · medium

   A data scientist is cleaning a dataset for a sentiment analysis model. The dataset contains text reviews with many typos, emojis, and inconsistent capitalization. The scientist wants to reduce noise while preserving the sentiment-bearing content. Which preprocessing step is most appropriate?

   • ARemove all stop words and apply stemming.
   • BApply spelling correction, convert emojis to text representations, and normalize case to lowercase.
   • CConvert all text to uppercase and remove numbers.
   • DRemove all punctuation and emojis, and convert all text to lowercase.
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4. 3foundation · easy

   A data scientist has two datasets: one with customer information and one with order information. Both contain a common column, CustomerID. Which merging method is used to combine these datasets by matching rows on CustomerID?

   • AAppend
   • BConcatenation
   • CPivot
   • DJoin
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5. 4expert · hard

   A data analyst is merging a dataset of customer transactions with a dataset of customer demographics. The transaction dataset has multiple rows per customer, while the demographics dataset has one row per customer. The analyst wants to create a dataset with one row per customer, including total transaction amount and demographic information. What should the analyst do?

   • AMerge the datasets on customer_id using an inner join, then drop duplicate customer rows.
   • BAggregate the transaction dataset by customer_id (e.g., sum of amount), then merge with the demographics dataset on customer_id.
   • CMerge the datasets on customer_id, then aggregate the merged dataset by customer_id.
   • DAggregate the demographics dataset by customer_id, then merge with the transaction dataset on customer_id.
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6. 5application · medium

   A data scientist is preparing a dataset for a credit risk model. The 'income' column has 20% missing values, and the missingness is completely random. The scientist wants to preserve the original distribution of income as much as possible. Which imputation method is most appropriate?

   • AMean imputation
   • BMedian imputation
   • CMode imputation
   • DMultiple imputation
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