API analytics and usage metrics in Rest API - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to retrieve the total number of API requests from the analytics endpoint.

Rest API

response = requests.get('https://api.example.com/analytics/usage')
total_requests = response.json().get([1])

Drag options to blanks, or click blank then click option'

ArequestCount

BtotalRequests

Ctotal_calls

DusageCount

Attempts:

3 left

💡 Hint

Common Mistakes

Using a key that does not exist in the response JSON.

Confusing total requests with usage count.

The key requestCount holds the total number of API requests in the JSON response.

2fill in blank

medium

Complete the code to filter API usage data for the last 7 days using query parameters.

Rest API

params = {'period': [1]
response = requests.get('https://api.example.com/analytics/usage', params=params)

Drag options to blanks, or click blank then click option'

A'last_month'

B'weekly'

C'last_7_days'

D'7days'

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect period strings like 'weekly' or '7days' which the API does not recognize.

Omitting quotes around the string value.

The API expects the period parameter as 'last_7_days' to filter data for the last week.

3fill in blank

hard

Fix the error in the code to correctly parse the JSON response and extract the average response time.

Rest API

data = response.[1]()
avg_response_time = data['metrics']['avgResponseTime']

Drag options to blanks, or click blank then click option'

Aparse_json

Btext

Ccontent

Djson

Attempts:

3 left

💡 Hint

Common Mistakes

Using text() which returns a string, not a dictionary.

Trying to access JSON data without parsing it first.

The json() method parses the response body as JSON, allowing access to nested data.

4fill in blank

hard

Complete the code to create a dictionary comprehension that maps each API endpoint to its total call count, filtering only endpoints with more than 100 calls.

Rest API

endpoint_calls = {endpoint: data[endpoint] for endpoint in data if data[endpoint] [1] 100}

Drag options to blanks, or click blank then click option'

B>

C>=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Adding an operator after data[endpoint] in the value part causing syntax errors.

Using less than operator which filters wrong endpoints.

The comprehension maps each endpoint to its call count directly (no operator needed after data[endpoint]) and filters endpoints with calls greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each API endpoint (in uppercase) to its average response time, including only endpoints with average response time less than 200 ms.

Rest API

fast_endpoints = [1]: data[endpoint]['avgResponseTime'] for endpoint in data if data[endpoint]['avgResponseTime'] [2] [3]

Drag options to blanks, or click blank then click option'

Aendpoint.upper()

B<

C200

Dendpoint.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using greater than operator which filters wrong endpoints.

Omitting the threshold value.

The comprehension uses endpoint.upper() as the key, filters average response times less than 200 ms using < and 200 as the threshold.