Why is it important to ensure consistent data types within a column of a spreadsheet?

To make the spreadsheet visually appealing

To prevent errors during analysis and ensure accurate calculations

To allow multiple values in a single cell

To make the spreadsheet file smaller

A dataset has a 'Revenue' column with a highly skewed distribution (most values are small, a few are very large). An analyst needs to prepare this column for a linear regression model. The model assumes normally distributed errors. Which transformation is most appropriate?

Apply a log transformation: log(Revenue + 1)

Remove all values above the 95th percentile

Apply Min-Max scaling to 0-1 range

A dataset has a 'Date' column as strings in the format 'MM/DD/YYYY'. An analyst needs to extract the year and month as separate integer columns for a time-series analysis. What is the most appropriate approach?

Use string slicing to extract characters 6-10 for year and 0-2 for month

Use df['Date'].str.split('/') and take the parts

Convert the column to datetime with pd.to_datetime, then use .dt.year and .dt.month

Keep the Date as string and use regex to extract year and month

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 1Objective 4

Data Preparation Techniques PCAD-31 Practice Questions (Page 2)

Part of the Data Acquisition and Pre-Processing domain, which accounts for 29.2% of the PCAD-31 exam. Python Institute does not publish an official question count, but from its 60-minute exam (~25–40 total, ~7–7 in this domain), expect 2–2 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

6concepts

29.2%of the exam

Questions 6–10

6foundation · easy
Which pandas function is used to read data from a CSV file into a DataFrame?
Select an answer first
7foundation · easy
Why is it important to ensure consistent data types within a column of a spreadsheet?
Select an answer first
8expert · hard
A dataset has a 'Revenue' column with a highly skewed distribution (most values are small, a few are very large). An analyst needs to prepare this column for a linear regression model. The model assumes normally distributed errors. Which transformation is most appropriate?
Select an answer first
9expert · hard
A dataset has a 'Date' column as strings in the format 'MM/DD/YYYY'. An analyst needs to extract the year and month as separate integer columns for a time-series analysis. What is the most appropriate approach?
Select an answer first
10foundation · easy
Which pandas function is used to read data from a SQL database into a DataFrame?
Select an answer first

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Data Preparation Techniques PCAD-31 Practice Questions (Page 2)

Questions 6–10

Which pandas function is used to read data from a CSV file into a DataFrame?

Why is it important to ensure consistent data types within a column of a spreadsheet?

A dataset has a 'Revenue' column with a highly skewed distribution (most values are small, a few are very large). An analyst needs to prepare this column for a linear regression model. The model assumes normally distributed errors. Which transformation is most appropriate?

A dataset has a 'Date' column as strings in the format 'MM/DD/YYYY'. An analyst needs to extract the year and month as separate integer columns for a time-series analysis. What is the most appropriate approach?

Which pandas function is used to read data from a SQL database into a DataFrame?

Keep studying — Data Acquisition and Pre-Processing