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Flaskframework~10 mins

Request validation in Flask - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to import the Flask class.

Flask
from flask import [1]
app = [1](__name__)
Drag options to blanks, or click blank then click option'
ARequest
Brender_template
CFlask
Djsonify
Attempts:
3 left
💡 Hint
Common Mistakes
Importing Request instead of Flask
Using lowercase 'flask' instead of 'Flask'
2fill in blank
medium

Complete the code to get JSON data from a POST request.

Flask
from flask import request

@app.route('/data', methods=['POST'])
def data():
    data = request.[1]
    return 'Received'
Drag options to blanks, or click blank then click option'
Ajson
Bform
Cheaders
Dargs
Attempts:
3 left
💡 Hint
Common Mistakes
Using request.args which is for URL parameters
Using request.form which is for form data
3fill in blank
hard

Fix the error in the code to validate a required JSON field 'name'.

Flask
from flask import request, abort

@app.route('/submit', methods=['POST'])
def submit():
    data = request.json
    if not data or '[1]' not in data:
        abort(400, 'Missing name')
    return 'OK'
Drag options to blanks, or click blank then click option'
Aemail
Busername
Cid
Dname
Attempts:
3 left
💡 Hint
Common Mistakes
Checking for a wrong key like 'username' or 'email'
Not checking if data is None before key check
4fill in blank
hard

Fill both blanks to extract 'age' from JSON and check if it is an integer.

Flask
data = request.json
age = data.get([1])
if not isinstance(age, [2]):
    abort(400, 'Age must be an integer')
Drag options to blanks, or click blank then click option'
A'age'
Bint
Cstr
D'name'
Attempts:
3 left
💡 Hint
Common Mistakes
Using wrong key like 'name' instead of 'age'
Checking type against 'str' instead of 'int'
5fill in blank
hard

Fill all three blanks to return a JSON error response with status 400.

Flask
from flask import jsonify, abort

@app.route('/check', methods=['POST'])
def check():
    data = request.json
    if not data or 'email' not in data:
        return jsonify([1]: [2]), [3]
    return 'Valid'
Drag options to blanks, or click blank then click option'
A'error'
B'Missing email'
C400
D'message'
Attempts:
3 left
💡 Hint
Common Mistakes
Using wrong JSON key like 'message' instead of 'error'
Returning status 200 instead of 400

Practice

(1/5)
1. What is the main purpose of request validation in a Flask application?
easy
A. To store user data permanently
B. To check incoming data for correctness and safety
C. To style the HTML forms automatically
D. To speed up the server response time

Solution

  1. Step 1: Understand what request validation means

    Request validation means checking the data sent by users to make sure it is correct and safe to use.
  2. Step 2: Identify the main goal of validation

    The main goal is to prevent bad or harmful data from causing errors or security problems.
  3. Final Answer:

    To check incoming data for correctness and safety -> Option B
  4. Quick Check:

    Request validation = check data safety [OK]
Hint: Validation means checking data is safe and correct [OK]
Common Mistakes:
  • Thinking validation speeds up the server
  • Confusing validation with styling
  • Assuming validation stores data
2. Which of the following is the correct way to access JSON data from a Flask request object?
easy
A. request.get_json()
B. request.json_data
C. request.json()
D. request.data.json

Solution

  1. Step 1: Recall Flask's method to get JSON data

    Flask provides the method get_json() to parse JSON from the request body.
  2. Step 2: Check each option for correctness

    request.json() uses parentheses but json is a property, not a method. request.json_data and D are not valid Flask attributes.
  3. Final Answer:

    request.get_json() -> Option A
  4. Quick Check:

    Use get_json() to read JSON data [OK]
Hint: Use request.get_json() to read JSON in Flask [OK]
Common Mistakes:
  • Calling request.json as a function
  • Using non-existent attributes like json_data
  • Trying to access JSON via request.data.json
3. Given this Flask route code, what will be the output if the JSON request body is {"age": 17}?
from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/check', methods=['POST'])
def check_age():
    data = request.get_json()
    if data and data.get('age', 0) >= 18:
        return jsonify({'status': 'allowed'})
    else:
        return jsonify({'status': 'denied'})
medium
A. {"status": "allowed"}
B. Empty response
C. 500 Internal Server Error
D. {"status": "denied"}

Solution

  1. Step 1: Analyze the JSON input and condition

    The input JSON has "age": 17. The code checks if age is 18 or more.
  2. Step 2: Determine which branch runs

    Since 17 is less than 18, the else branch runs, returning {'status': 'denied'}.
  3. Final Answer:

    {"status": "denied"} -> Option D
  4. Quick Check:

    Age 17 < 18 = denied [OK]
Hint: Check if age >= 18 to allow, else deny [OK]
Common Mistakes:
  • Assuming 17 is allowed
  • Expecting server error for valid JSON
  • Thinking response is empty
4. What is wrong with this Flask code snippet for validating a required 'username' field in JSON?
data = request.get_json()
if 'username' in data:
    return 'Username received'
else:
    return 'Missing username', 400
medium
A. It does not check if data is None before accessing
B. It uses wrong HTTP status code for error
C. It should use request.json instead of get_json()
D. It should return JSON response, not plain text

Solution

  1. Step 1: Understand get_json() behavior

    request.get_json() can return None if no JSON is sent or invalid JSON.
  2. Step 2: Identify the error in code

    The code accesses 'username' in data without checking if data is None, causing a crash if data is None.
  3. Final Answer:

    It does not check if data is None before accessing -> Option A
  4. Quick Check:

    Check data is not None before key access [OK]
Hint: Always check if get_json() returned data before use [OK]
Common Mistakes:
  • Ignoring None check on JSON data
  • Confusing status codes
  • Thinking request.json is better than get_json()
  • Assuming plain text is always wrong
5. You want to validate a JSON request in Flask to ensure it has a 'name' string and an 'age' integer above 0. Which approach best combines validation and clear error messages?
hard
A. Manually check keys and types, return custom error messages on failure
B. Use request.get_json() and assume data is always correct
C. Use a library like marshmallow to define a schema and validate automatically
D. Skip validation and handle errors later in the code

Solution

  1. Step 1: Consider manual validation drawbacks

    Manual checks can be error-prone and verbose, making code hard to maintain.
  2. Step 2: Understand benefits of marshmallow

    Marshmallow lets you define clear schemas and automatically validates input, returning helpful error messages.
  3. Step 3: Evaluate other options

    Assuming data is correct or skipping validation risks bugs and security issues.
  4. Final Answer:

    Use a library like marshmallow to define a schema and validate automatically -> Option C
  5. Quick Check:

    Use marshmallow for clear, automatic validation [OK]
Hint: Use marshmallow for easy, clear validation [OK]
Common Mistakes:
  • Assuming data is always valid
  • Writing complex manual checks
  • Ignoring validation completely
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10 questions~5 mineasy hard