What Does This Python Script Achieve When Parsing JSON Data?

Refer to the exhibit. What is achieved by this Python script? - image

  1. It loads JSON data into an HTTP request.
  2. It converts JSON data to an HTML document.
  3. It counts JSON data from a website.
  4. It reads JSON data into a formatted list. Source Reference Answer

Community Votes

D
100%

100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

It tests knowledge of Python's json.loads() function and list population logic, with the common trap being confusion between JSON parsing, HTML generation, and basic request construction.

This question evaluates a Python script that fetches JSON data via an HTTP GET request and deserializes it into a native Python data structure. The community unanimously confirms the script successfully reads and formats the JSON response into a structured list.

Candidates frequently choose option B (HTML conversion) or A (loading into an HTTP request) because they overlook how json.loads() deserializes strings into Python objects, mistakenly assuming web-facing scripts must output HTML or only handle outgoing request headers.

Community Discussion (4 comments)

teems5uk 👍 6 Selected: D
D. It reads JSON data into a formatted list. The script makes HTTP requests to a specified URL, retrieves JSON data, and processes it to create a formatted list. In this case, it appears to be extracting version information and a list of bad IP addresses from a web service.
AbdullahMohammad251 👍 1 Selected: D
We are appending the response content of an HTTP GET request into a Python list "bp"
chiacche 👍 1 Selected: D
Sends an HTTP GET request to the badip endpoint and appends the 'ip' value of each bad IP to the bgp list
nj1999 👍 3
D What are JSON loads () in Python? The json.loads() method can be used to parse a valid JSON string and convert it into a Python Dictionary. It is mainly used for deserializing native string, byte, or byte array which consists of JSON data into Python Dictionary.

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Expert Analysis

Core Concept: JSON Parsing in Python

The script demonstrates a foundational network automation pattern: retrieving external data via REST APIs and processing it programmatically. Using the requests library, the code sends an HTTP GET request to a defined endpoint. The raw response body arrives as a JSON-formatted string, which is immediately passed to json.loads(). This built-in function deserializes the JSON string into native Python data structures, typically converting JSON objects into Python dictionaries and arrays into lists.

Why Option D is Correct

As highlighted by community contributors, the script iterates through the parsed JSON structure, extracts specific field values (such as IP addresses), and appends them to a new Python list. This directly aligns with option D: "It reads JSON data into a formatted list." The combination of requests.get() and json.loads() is the industry-standard method for transforming API payloads into actionable Python variables.

Why Other Options Are Incorrect

  • Option A incorrectly suggests loading data into an HTTP request. While the requests library manages outgoing traffic, this script focuses on consuming and processing an incoming response payload locally.
  • Option B confuses JSON parsing with HTML generation. No template engines, string concatenation for markup, or web framework functions are present; json.loads() outputs Python dictionaries and lists, not rendered web pages.
  • Option C implies a pure counting operation. Although the script may iterate through items, its primary objective is data extraction and storage within a list, not merely calculating a numerical count.

Community Validation

Multiple candidates emphasized that json.loads() converts JSON strings into Python dictionaries, which are then traversed to build the target list. Others confirmed the script systematically appends extracted fields like 'ip' to a variable such as bgp or bp, reinforcing the list-building mechanism over alternative interpretations.

Official Reference

Exam Strategy

When analyzing scripting questions, always trace the data flow step-by-step: identify the input source, the transformation function, and the final output variable. Focus on recognizing key library methods like json.loads() or .append() to quickly eliminate distractors that describe unrelated operations like HTML rendering or request construction.

Related Analysis

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