- A
df.drop_duplicates()
Why wrong: Removes duplicate rows, not missing values.
- B
df.dropna()
Removes rows with any missing values by default.
- C
df.fillna(0)
Why wrong: Fills missing values with 0, does not remove rows.
- D
df.isna()
Why wrong: Returns a DataFrame of booleans indicating missing values.
Question 924 of 1,000
DA0-001 Mining Data Practice Question
This DA0-001 practice question tests your understanding of mining 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.
You are using pandas in Python to clean a dataset. You notice several rows with missing values in the 'age' column. Which method would you use to remove those rows?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
df.dropna()
df.dropna() removes rows with any missing values by default. df.fillna() fills missing values, df.isna() returns a boolean mask, df.drop_duplicates() removes duplicate rows.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
df.drop_duplicates()
Why it's wrong here
Removes duplicate rows, not missing values.
- ✓
df.dropna()
Why this is correct
Removes rows with any missing values by default.
Related concept
CIDR notation defines the prefix length.
- ✗
df.fillna(0)
Why it's wrong here
Fills missing values with 0, does not remove rows.
- ✗
df.isna()
Why it's wrong here
Returns a DataFrame of booleans indicating missing values.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related DA0-001 subnetting questions on CIDR, address ranges, and subnet selection.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
Mining Data — This question tests Mining Data — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: df.dropna() — df.dropna() removes rows with any missing values by default. df.fillna() fills missing values, df.isna() returns a boolean mask, df.drop_duplicates() removes duplicate rows.
What should I do if I get this DA0-001 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related DA0-001 subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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Last reviewed: Jul 4, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-001 exam.
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