Data Preparation Techniques PCAD-31 Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   An analyst is cleaning a spreadsheet that has inconsistent column headers: 'Name', 'name', and 'NAME' in different columns. What is the best practice for standardizing these headers?

   • ADelete the columns with non-standard headers
   • BRename all headers to lowercase with underscores, e.g., 'name'
   • CMerge the columns into one and use the first header found
   • DKeep the headers as they are since they refer to the same data
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2. 17expert · hard

   An analyst is preparing a final report spreadsheet for a non-technical audience. The data includes a 'Timestamp' column in ISO format (e.g., '2024-03-15T14:30:00Z') and a 'Category' column with values like 'Electronics', 'electronics', and 'ELECTRONICS'. What is the most important step to ensure the spreadsheet is readable and consistent?

   • AKeep the ISO timestamp as-is and leave Category values unchanged
   • BConvert Category to numeric codes and keep the ISO timestamp
   • CSplit the Timestamp into separate Date and Time columns, but leave Category as-is
   • DConvert the Timestamp to a readable date format and standardize the Category values to a consistent case
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4. 18expert · hard

   An analyst needs to combine data from two sources: a CSV file on a local drive and a JSON API. Both contain customer information, but the CSV has 'cust_id' and the API has 'customer_id'. The analyst wants to merge them into one DataFrame. What is the most appropriate sequence of steps?

   • ALoad the API data first, then overwrite the CSV data
   • BLoad both datasets, rename 'customer_id' to 'cust_id' in the API data, then merge on 'cust_id'
   • CLoad the CSV, then append the API data as new rows without merging
   • DLoad both datasets and merge directly on the different column names
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5. 19application · medium

   An analyst needs to fetch data from a REST API that returns JSON. The API requires an API key in the header. Which approach should the analyst use?

   • AUse the requests library to send a GET request with the API key in the headers, then parse the JSON response
   • BUse urllib.request.urlopen with the API key in the URL
   • CUse pd.read_json('https://api.example.com/data') directly
   • DUse pd.read_csv('https://api.example.com/data?key=YOUR_KEY')
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6. 20application · medium

   A DataFrame has a 'Gender' column with values 'M', 'F', 'Male', 'Female', and 'Unknown'. An analyst wants to clean this column for analysis. Which step should be taken first?

   • AConvert the column to numeric using one-hot encoding
   • BStandardize the values by mapping 'M' and 'Male' to 'Male', and 'F' and 'Female' to 'Female'
   • CDrop the 'Unknown' values immediately
   • DFill all missing values with 'M'
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