Document loading and parsing in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this document parsing code?

Consider the following code that loads and parses a JSON document string. What will be the value of parsed_data after running this code?

Prompt Engineering / GenAI

import json
json_string = '{"name": "Alice", "age": 30, "skills": ["Python", "ML"]}'
parsed_data = json.loads(json_string)

A {'name': 'Alice', 'age': 30, 'skills': ['Python', 'ML']}

B {"name": "Alice", "age": 30, "skills": ["Python", "ML"]}

C {'name': "Alice", 'age': 30, 'skills': ["Python", "ML"]}

D {"name": 'Alice', "age": 30, "skills": ['Python', 'ML']}

💡 Hint

Remember that json.loads converts JSON strings into Python dictionaries with single quotes.

The json.loads function converts the JSON string into a Python dictionary. Python dictionaries use single quotes for string keys and values when printed.

🧠 Conceptual

intermediate

Which format is best for loading structured text documents for ML?

You want to load a large collection of structured text documents for machine learning. Which document format is most suitable for easy parsing and extracting fields?

A Binary files with custom encoding

B Plain text files with no structure

C Encrypted PDF files

D CSV files with columns for each field

💡 Hint

Think about formats that clearly separate data fields for easy extraction.

CSV files organize data in rows and columns, making it easy to parse and extract fields for ML tasks.

❓ Metrics

advanced

How to measure parsing success rate on a document dataset?

You have a dataset of 1000 documents to parse. Your parser successfully extracts data from 920 documents without errors. What is the parsing success rate?

A 100%

B 8%

C 92%

D 90%

💡 Hint

Success rate = (number of successful parses / total documents) * 100

Parsing success rate is the percentage of documents parsed without errors: (920/1000)*100 = 92%.

🔧 Debug

advanced

Why does this XML parsing code raise an error?

This code tries to parse an XML document but raises an error. What is the cause?

Prompt Engineering / GenAI

import xml.etree.ElementTree as ET
xml_string = '<root><item>Value</item></root>'
root = ET.parse(xml_string)

A ET.parse expects a file path or file object, not a string of XML content

B The XML string is malformed and missing closing tags

C ET.parse cannot parse XML with nested tags

D The xml.etree.ElementTree module is not imported correctly

💡 Hint

Check the expected input type for ET.parse.

ET.parse expects a filename or file object, not a string containing XML data. To parse a string, use ET.fromstring instead.

❓ Model Choice

expert

Which model is best for extracting structured data from scanned document images?

You have scanned images of invoices and want to extract structured fields like date, total, and vendor name. Which AI model type is best suited for this task?

A Convolutional Neural Network (CNN) for image classification

B Optical Character Recognition (OCR) combined with Named Entity Recognition (NER)

C Generative Adversarial Network (GAN) for image generation

D Recurrent Neural Network (RNN) for time series prediction

💡 Hint

Think about models that convert images to text and then extract information.

OCR converts images to text, and NER extracts structured fields from text, making this combination ideal for scanned document data extraction.

Practice

1. What is the main purpose of document loading in AI projects?

easy

A. To clean the data by removing errors

B. To train the AI model with labeled data

C. To visualize the results of the AI model

D. To read text files so the computer can access their content

Solution

1. Step 1: Understand document loading

   Document loading means reading text files so the computer can access the content inside.

2. Step 2: Differentiate from other tasks

   Training models, visualization, and cleaning are different steps after loading the document.

3. Final Answer:

   To read text files so the computer can access their content -> Option D

4. Quick Check:

   Document loading = reading files [OK]

Hint: Loading means reading files into the computer [OK]

Common Mistakes:

• Confusing loading with training the model
• Thinking loading cleans the data
• Mixing loading with visualization

2. Which Python code snippet correctly loads a text file named data.txt into a string variable?

easy

A. with open('data.txt', 'x') as file:
text = file.read()

B. file = open('data.txt', 'w')
text = file.read()

C. with open('data.txt', 'r') as file:
text = file.read()

D. text = open('data.txt').write()

Solution

1. Step 1: Check file mode for reading

   Mode 'r' opens the file for reading, which is needed to load text.

2. Step 2: Use context manager and read method

   Using with open(...) ensures safe file handling, and file.read() reads all content.

3. Final Answer:

   with open('data.txt', 'r') as file: text = file.read() -> Option C

4. Quick Check:

   Open with 'r' and read() = correct loading [OK]

Hint: Use 'r' mode and read() to load text files [OK]

Common Mistakes:

• Using 'w' mode which is for writing, not reading
• Calling write() instead of read()
• Using 'x' mode which is for creating new files

3. What will be the output of this Python code that parses a loaded text?

text = "Hello world! Welcome to AI."
words = text.split()
print(words)

medium

A. ['Hello', 'world', 'Welcome', 'to', 'AI']

B. ['Hello', 'world!', 'Welcome', 'to', 'AI.']

C. ['Hello world! Welcome to AI.']

D. ['H', 'e', 'l', 'l', 'o']

Solution

1. Step 1: Understand split() method

   The split() method splits the string by spaces into a list of words, keeping punctuation attached.

2. Step 2: Apply split() to the text

   Splitting "Hello world! Welcome to AI." results in ['Hello', 'world!', 'Welcome', 'to', 'AI.'] including punctuation.

3. Final Answer:

   ['Hello', 'world!', 'Welcome', 'to', 'AI.'] -> Option B

4. Quick Check:

   split() by space keeps punctuation attached [OK]

Hint: split() breaks text by spaces, punctuation stays [OK]

Common Mistakes:

• Expecting punctuation to be removed automatically
• Thinking split() returns a single string list
• Confusing split() with list(text) which splits characters

4. Identify the error in this code that tries to parse a document into sentences:

text = "AI is fun. Let's learn it."
sentences = text.split('. ')
print(sentences)

medium

A. The split delimiter '. ' misses the last sentence ending

B. The code should use splitlines() instead of split()

C. The print statement is missing parentheses

D. The variable name 'sentences' is invalid

Solution

1. Step 1: Analyze split delimiter usage

   Splitting by '. ' splits sentences but leaves the last sentence without a trailing '. ' unseparated.

2. Step 2: Understand effect on last sentence

   The last sentence "Let's learn it." remains attached with the period, causing inconsistent splitting.

3. Final Answer:

   The split delimiter '. ' misses the last sentence ending -> Option A

4. Quick Check:

   Splitting by '. ' misses last sentence split [OK]

Hint: Splitting by '. ' misses last sentence if no trailing space [OK]

Common Mistakes:

• Thinking splitlines() splits sentences
• Forgetting print() needs parentheses in Python 3
• Assuming variable names cause errors

5. You have a text file with multiple paragraphs separated by blank lines. Which approach best loads and parses it into a list of paragraphs for AI processing?

hard

A. Read the file, split text by double newlines '\n\n', then strip whitespace from each paragraph

B. Read the file line by line and treat each line as a paragraph

C. Use split() to split by single spaces to get paragraphs

D. Load the file and convert all text to uppercase without splitting

Solution

1. Step 1: Understand paragraph separation

   Paragraphs are separated by blank lines, which means two newline characters '\n\n'.

2. Step 2: Parse paragraphs correctly

   Splitting by '\n\n' divides text into paragraphs; stripping whitespace cleans each paragraph.

3. Final Answer:

   Read the file, split text by double newlines '\n\n', then strip whitespace from each paragraph -> Option A

4. Quick Check:

   Split by '\n\n' for paragraphs [OK]

Hint: Paragraphs split by double newlines '\n\n' [OK]

Common Mistakes:

• Splitting by single spaces splits words, not paragraphs
• Treating each line as a paragraph loses multi-line paragraphs
• Ignoring whitespace cleanup after splitting