Data Validation and Integrity PCAD-31 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A retail analytics team receives daily CSV exports from 200 store locations. Each file has columns: store_id, transaction_date, amount, and currency. The team notices that some files contain rows where amount is negative, and a few rows have transaction_date formatted as 'YYYY-MM-DD' while others use 'MM/DD/YYYY'. The data must be loaded into a single DataFrame for reporting. What is the most appropriate validation approach to apply during ingestion?

   • AConvert all transaction_date values to strings and concatenate all files without further checks.
   • BCheck that store_id is numeric, amount is non-negative, and transaction_date parses to a consistent datetime format; reject or flag rows that fail.
   • COnly verify that the number of columns in each file matches the expected schema.
   • DDrop all rows with negative amounts and then load the files, assuming the date formats are acceptable.
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2. 2application · medium

   A university's registrar office collects student enrollment data. Each record must have a valid student_id (existing in the student master table) and an enrollment_date that is not in the future. What is the most appropriate validation strategy?

   • AConvert enrollment_date to a string to avoid date comparison issues.
   • BAllow any enrollment_date as long as student_id is unique.
   • CCheck that student_id is numeric and enrollment_date is not null.
   • DPerform a referential integrity check against the student master table and a date check that rejects future dates.
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4. 3application · medium

   A marketing team collects customer feedback via an online form. The form includes a 'rating' field (1–5 stars) and a 'comments' field (free text). The team wants to ensure that the rating is always an integer between 1 and 5, and that comments are not empty when the rating is 1. Which validation approach should be used?

   • ACheck that rating is not null and comments is not null.
   • BOnly check that comments have a maximum length of 500 characters.
   • CConvert rating to a float and allow any value between 0 and 10.
   • DApply a range check on rating (1–5) and a conditional rule that requires comments when rating equals 1.
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5. 4application · medium

   A subscription service stores customer records. The 'subscription_type' column must be one of 'basic', 'premium', or 'enterprise'. The 'monthly_fee' must be greater than 0. A data analyst discovers rows with subscription_type 'gold' and monthly_fee of -5. What is the best way to enforce integrity for future data entries?

   • AAllow any subscription_type but ensure monthly_fee is not null.
   • BOnly check that monthly_fee is a float data type.
   • CDefine a validation rule that checks subscription_type against a whitelist and monthly_fee against a range (greater than 0).
   • DMap unknown subscription types to 'basic' and set negative fees to 0.
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6. 5foundation · easy

   A data analyst is reviewing a pandas DataFrame and needs to identify which columns contain missing values. Which pandas method is specifically designed to return a Boolean mask indicating the presence of missing values?

   • Adf.isnull()
   • Bdf.dropna()
   • Cdf.unique()
   • Ddf.fillna()
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