Quick Answer
The answer is `json_normalize()`, the pandas library function designed specifically for JSON normalization to tabular format. This function excels at flattening semi-structured data, automatically expanding nested lists and dictionaries into separate rows and columns within a DataFrame, which is exactly what a data analyst needs when extracting data from an API that returns JSON. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of how to handle real-world API responses, where data is rarely flat—a common trap is confusing `json_normalize()` with basic JSON parsing functions like `json.loads()`, which only convert JSON to Python objects without flattening nested structures. The key distinction is that `json_normalize()` handles the hierarchical nature of JSON, making it the go-to tool for converting complex API outputs into analysis-ready tables. Memory tip: think "normalize" as in "normalize the nesting"—if your JSON has layers, `json_normalize()` flattens them into a clean spreadsheet.
This DA0-001 practice question tests your understanding of mining and acquiring 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.
A data analyst needs to extract data from an API that returns JSON. The analyst wants to convert the JSON output into a tabular format for analysis. Which function in a scripting language is commonly used for this purpose?
json.loads()
Why wrong: This parses JSON but does not flatten it into a table.
to_csv()
Why wrong: This exports data to CSV, not for parsing.
read_json()
Why wrong: This reads JSON into a DataFrame but does not handle nested structures automatically.
json_normalize()
This function normalizes semi-structured JSON data into a flat table.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
json_normalize()
Option D is correct because `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
json.loads()
Why it's wrong here
This parses JSON but does not flatten it into a table.
to_csv()
Why it's wrong here
This exports data to CSV, not for parsing.
read_json()
Why it's wrong here
This reads JSON into a DataFrame but does not handle nested structures automatically.
json_normalize()
Why this is correct
This function normalizes semi-structured JSON data into a flat table.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse `read_json()` (which works only for flat JSON) with `json_normalize()` (which handles nested structures), leading them to choose option C when the API response contains hierarchical data.
Detailed technical explanation
Under the hood, `json_normalize()` uses recursive logic to unpack nested dictionaries and lists, creating separate columns for each key path (e.g., `address.city`). It also supports a `record_path` parameter to handle arrays of records and a `meta` parameter to include sibling fields, which is critical when dealing with API responses that embed metadata alongside data arrays. In real-world scenarios, such as extracting user profiles from a REST API where each user has a nested `address` object, `json_normalize()` automatically expands the address fields into columns like `address.street` and `address.zip`, whereas `read_json()` would leave them as dictionaries.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Mining and Acquiring Data — This question tests Mining and Acquiring Data — Read the scenario before looking for a memorised answer..
The correct answer is: json_normalize() — Option D is correct because `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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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