A dataset contains a 'Color' column with values 'red', 'green', and 'blue'. An analyst wants to use this categorical variable in a machine learning model. Which transformation is most appropriate?

Replace each color with its RGB value as a single number

Apply one-hot encoding to create binary columns for each color

Use label encoding with alphabetical order (blue=0, green=1, red=2)

Map the colors to integers 1, 2, and 3

An analyst uses pd.read_csv('data.csv') and then calls df.head(). The output shows that the first row of the file contains column names, but the DataFrame index starts at 0 as expected. However, the analyst notices that the 'Date' column is being read as object dtype instead of datetime. What should the analyst do to fix this during the initial load?

Set header=None in read_csv and manually assign column names

Use df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d') after loading

Add parse_dates=['Date'] to the read_csv call

Call df['Date'] = df['Date'].astype(str) after loading

An analyst needs to load a dataset from a URL: 'https://example.com/data/report.json'. The file is a JSON array of objects. Which pandas function should the analyst use?

pd.read_html('https://example.com/data/report.json')

pd.read_csv('https://example.com/data/report.json')

pd.read_excel('https://example.com/data/report.json')

pd.read_json('https://example.com/data/report.json')

What is the purpose of one-hot encoding in data transformation?

To scale numerical values to a range

To convert categorical variables into binary columns

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 1Objective 4

Data Preparation Techniques PCAD-31 Practice Questions (Page 1)

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 1–5

1foundation · easy
A data file contains records separated by commas, with each record on a new line and an optional header row. Which file format does this describe?
Select an answer first
2application · medium
A dataset contains a 'Color' column with values 'red', 'green', and 'blue'. An analyst wants to use this categorical variable in a machine learning model. Which transformation is most appropriate?
Select an answer first
3application · medium
An analyst uses pd.read_csv('data.csv') and then calls df.head(). The output shows that the first row of the file contains column names, but the DataFrame index starts at 0 as expected. However, the analyst notices that the 'Date' column is being read as object dtype instead of datetime. What should the analyst do to fix this during the initial load?
Select an answer first
4application · easy
An analyst needs to load a dataset from a URL: 'https://example.com/data/report.json'. The file is a JSON array of objects. Which pandas function should the analyst use?
Select an answer first
5foundation · easy
What is the purpose of one-hot encoding in data transformation?
Select an answer first

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Data Preparation Techniques PCAD-31 Practice Questions (Page 1)

Questions 1–5

A data file contains records separated by commas, with each record on a new line and an optional header row. Which file format does this describe?

A dataset contains a 'Color' column with values 'red', 'green', and 'blue'. An analyst wants to use this categorical variable in a machine learning model. Which transformation is most appropriate?

An analyst needs to load a dataset from a URL: 'https://example.com/data/report.json'. The file is a JSON array of objects. Which pandas function should the analyst use?

What is the purpose of one-hot encoding in data transformation?

Keep studying — Data Acquisition and Pre-Processing