API-based deployment in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to send a POST request to the API endpoint.

Prompt Engineering / GenAI

response = requests.[1]('https://api.example.com/predict', json=data)

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

Apost

Bget

Cput

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using GET instead of POST causes the server to not receive data.

Using PUT or DELETE when only POST is accepted.

The POST method is used to send data to the API for prediction.

2fill in blank

medium

Complete the code to extract the JSON response from the API call.

Prompt Engineering / GenAI

result = response.[1]()

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

Ajson

Bcontent

Ctext

Dstatus_code

Attempts:

3 left

💡 Hint

Common Mistakes

Using response.text returns raw string, not parsed JSON.

Using response.status_code returns the HTTP status, not data.

The json() method parses the response content as JSON.

3fill in blank

hard

Fix the error in the code to handle API errors properly.

Prompt Engineering / GenAI

if response.status_code != [1]:
    print('Error:', response.status_code)

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

A201

B404

C500

D200

Attempts:

3 left

💡 Hint

Common Mistakes

Checking for 404 or 500 instead of 200 causes false error detection.

Using 201 which means resource created, not general success.

Status code 200 means the request was successful.

4fill in blank

hard

Fill both blanks to prepare headers and send the API request with authentication.

Prompt Engineering / GenAI

headers = {'Authorization': 'Bearer [1]'}
response = requests.post(url, json=data, headers=[2])

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

Aapi_key_123

Bdata

Cheaders

Dtoken_abc

Attempts:

3 left

💡 Hint

Common Mistakes

Passing data instead of headers causes authentication failure.

Using wrong token string in Authorization header.

The Authorization header needs the token, and headers must be passed as the headers argument.

5fill in blank

hard

Complete the code to parse the prediction from the API response dictionary.

Prompt Engineering / GenAI

prediction = result['output'[1]0]

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

A[

B][

D]

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of brackets causes errors.

Missing brackets leads to wrong data access.

Access the 'output' key with [ ] and then the first element with [0].

Practice

1. What is the main purpose of API-based deployment in AI?

easy

A. To train AI models faster on local machines

B. To share AI models as easy-to-use services over the internet

C. To store large datasets securely

D. To visualize AI model results on graphs

Solution

1. Step 1: Understand API-based deployment

   API-based deployment allows AI models to be accessed remotely as services.

2. Step 2: Identify the main purpose

   This means apps can get predictions without running the model themselves, making sharing easy.

3. Final Answer:

   To share AI models as easy-to-use services over the internet -> Option B

4. Quick Check:

   API deployment = share models online [OK]

Hint: API deployment means sharing models as services online [OK]

Common Mistakes:

• Confusing deployment with training
• Thinking API stores data
• Assuming API is for visualization only

2. Which Python library is commonly used to create a simple API server for deploying AI models?

easy

A. NumPy

B. Matplotlib

C. Flask

D. Pandas

Solution

1. Step 1: Recall common Python libraries

   NumPy is for math, Pandas for data, Matplotlib for plots, Flask for web servers.

2. Step 2: Identify the API server library

   Flask is a lightweight web framework used to create APIs easily.

3. Final Answer:

   Flask -> Option C

4. Quick Check:

   Flask = simple API server [OK]

Hint: Flask is the go-to for simple Python APIs [OK]

Common Mistakes:

• Choosing data libraries instead of web frameworks
• Confusing Flask with data processing tools
• Thinking Matplotlib creates APIs

3. Given this Flask API code snippet, what will be the output when sending a POST request with JSON {"input": 5}?

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    x = data['input']
    result = x * 2
    return jsonify({'output': result})

if __name__ == '__main__':
    app.run()

medium

A. {"output": 10}

B. {"output": 5}

C. {"output": 25}

D. Error: Missing input key

Solution

1. Step 1: Understand the input and processing

   The API receives JSON with key 'input' and value 5, then multiplies it by 2.

2. Step 2: Calculate the output

   5 * 2 = 10, so the output JSON will have 'output': 10.

3. Final Answer:

   {"output": 10} -> Option A

4. Quick Check:

   Input 5 doubled = 10 [OK]

Hint: Multiply input by 2 as per code logic [OK]

Common Mistakes:

• Confusing input and output values
• Assuming output equals input
• Missing JSON key causes error

4. Identify the error in this Flask API code for deploying a model:

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json()
    x = data['input']
    result = x + 1
    return jsonify({'output': result})

if __name__ == '__main__':
    app.run()

medium

A. Incorrect HTTP method, should be GET

B. Missing route decorator

C. Returning plain string instead of JSON

D. Using request.json() instead of request.get_json()

Solution

1. Step 1: Check how JSON is accessed in Flask

   Flask's request object uses get_json() method, not json() function.

2. Step 2: Identify the error

   Using request.json() will cause an AttributeError; correct is request.get_json().

3. Final Answer:

   Using request.json() instead of request.get_json() -> Option D

4. Quick Check:

   Use get_json() to parse JSON [OK]

Hint: Use request.get_json(), not request.json() [OK]

Common Mistakes:

• Confusing request.json with get_json()
• Changing HTTP method unnecessarily
• Forgetting route decorator

5. You want to deploy a machine learning model via an API that predicts house prices. The model expects a JSON with features like 'size' and 'bedrooms'. Which approach best ensures your API handles missing features gracefully?

hard

A. Fill missing features with default values before prediction

B. Return an error if any feature is missing without processing

C. Ignore missing features and predict with available data only

D. Restart the server to reset missing data

Solution

1. Step 1: Understand missing feature handling

   Missing features can cause prediction errors if not handled properly.

2. Step 2: Choose a robust approach

   Filling missing features with default or average values allows prediction to continue safely.

3. Final Answer:

   Fill missing features with default values before prediction -> Option A

4. Quick Check:

   Use defaults for missing inputs [OK]

Hint: Use default values to handle missing features [OK]

Common Mistakes:

• Stopping API on missing data
• Ignoring missing features causing errors
• Restarting server unrelated to missing data