The answer is that the code reads the data, adds a calculated column, and shows the first 5 rows. This is correct because the pandas DataFrame `read_csv()` loads the dataset, then a new column like `'total'` is created by summing existing columns—such as `df['total'] = df['col1'] + df['col2']`—which performs element-wise addition across the rows. Finally, calling `.head()` without an argument defaults to displaying the first five rows of the DataFrame. On the CompTIA Data+ DA0-001 exam, this question tests your ability to trace the sequence of pandas operations, a common task in data transformation scenarios. A frequent trap is confusing `.head()` with `.tail()` or assuming it shows all rows; remember that `.head()` always returns the top rows by default. For a quick memory tip: think “Head starts at the top, Tail ends at the bottom”—and a calculated column is just a new column derived from existing data using arithmetic or functions.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Refer to the exhibit.
import pandas as pd
df = pd.read_csv('data.csv')
df['total'] = df['price'] * df['quantity']
df.head()
A data analyst runs the Python code shown. What is the result of executing this code?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It reads the data, adds a calculated column, and shows the first 5 rows
The code reads a CSV file into a pandas DataFrame, then creates a new column 'total' by summing columns 'col1' and 'col2'. Finally, `head()` returns the first 5 rows. Option A correctly describes this sequence of operations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
It reads the data, adds a calculated column, and shows the first 5 rows
Why this is correct
The code does exactly that.
Related concept
Read the scenario before looking for a memorised answer.
✗
It throws an error because 'total' column already exists
Why it's wrong here
If column exists, it would be overwritten; no error.
✗
It reads the data and displays all rows
Why it's wrong here
head() displays only first 5 rows.
✗
It reads the data and displays summary statistics
Why it's wrong here
head() does not produce summary statistics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think `head()` shows all rows or that adding a column with an existing name throws an error, but pandas silently overwrites the column.
Detailed technical explanation
How to think about this question
Under the hood, pandas uses vectorized operations for column arithmetic, making `df['total'] = df['col1'] + df['col2']` efficient even on large datasets. The `head()` method returns a new DataFrame slice, not a view, so modifications to the result do not affect the original. In real-world ETL pipelines, this pattern is common for feature engineering before model training.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: It reads the data, adds a calculated column, and shows the first 5 rows — The code reads a CSV file into a pandas DataFrame, then creates a new column 'total' by summing columns 'col1' and 'col2'. Finally, `head()` returns the first 5 rows. Option A correctly describes this sequence of operations.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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