Let’s face it training modules like the Walmart Anti-Money Laundering (AML) CBL (Computer Based Learning) can be dry, tedious, and full of jargon. But what if you could build your own tool using Python to help you navigate, understand, and practice for that CBL, I’ll walk you through how to build a mini-project: a “Walmart AML CBL answers” study-assistant tool using Python.
Project Overview What Our Tool Does
The idea:
We’ll build a Python script (or set of scripts) that:
- Loads a set of questions and correct answers related to the Walmart AML CBL (you’ll fill these in).
- Offers the user a quiz mode: randomly picks a question, prompts you, you type in an answer (or pick from multiple choice).
- Tracks your score, remembers which questions you got wrong, allows you to review them.
- Optionally lets you add new questions or edit existing ones.
- Optionally saves your history (how you performed) so you can see your improvement.
Why this matters:
- Instead of passively reading the training slides, you actively practice. That improves retention.
- You can tailor it to your role at Walmart (if you work there) or tailor it to key concepts you struggle with.
- It’s in Python, so you also learn practical coding skills (which is a bonus).
- It gives you a sense of control and progress (versus just clicking “next” in a training module).
What you’ll need:
- Python 3 installed (most computers already).
- Basic understanding of running a script.
- A text editor.
- (Optional) For saving history: you might use a simple file or even a light database like SQLite.
- When you’re ready, you can expand to a GUI or web interface but we’ll keep it simple here.
Code Construction
Setup and Question Storage:
First, we need to store the questions and answers. A simple way is a JSON or CSV file. Example: questions.json.
[
{
"id": 1,
"question": "What does CDD mean in the AML context?",
"choices": ["Customer Due Diligence", "Counter Drug Disclosure", "Cash Deposit Directive", "Customer Data Declaration"],
"answer": "Customer Due Diligence"
},
{
"id": 2,
"question": "What is a common red-flag for money laundering at a retailer like Walmart?",
"choices": ["Large cash transaction with no receipt", "Frequent small card purchases", "Buying only pencils", "Returning items late"],
"answer": "Large cash transaction with no receipt"
}
]
You can fill in as many as you like. Save it as questions.json.
Loading Questions & Basic Quiz Logic:
Here’s quiz.py:
import json
import random
import os
QUESTIONS_FILE = "questions.json"
HISTORY_FILE = "history.json"
def load_questions():
with open(QUESTIONS_FILE, "r") as f:
return json.load(f)
def save_history(history):
with open(HISTORY_FILE, "w") as f:
json.dump(history, f, indent=2)
def load_history():
if os.path.exists(HISTORY_FILE):
with open(HISTORY_FILE, "r") as f:
return json.load(f)
else:
return {}
def quiz_user(questions):
correct = 0
wrong_list = []
random.shuffle(questions)
for q in questions:
print("\nQ:", q["question"])
for idx, choice in enumerate(q["choices"], start=1):
print(f" {idx}. {choice}")
ans = input("Your answer (number): ")
try:
ans_idx = int(ans) - 1
chosen = q["choices"][ans_idx]
except:
print("Invalid choice. Skipping question.")
continue
if chosen == q["answer"]:
print("Correct!")
correct += 1
else:
print("Wrong. Correct answer:", q["answer"])
wrong_list.append(q)
print(f"\nYour score: {correct} out of {len(questions)}")
return {"correct": correct, "total": len(questions), "wrong": wrong_list}
def main():
questions = load_questions()
history = load_history()
result = quiz_user(questions)
# Simple history tracking: just store latest attempt
history["last_attempt"] = result
save_history(history)
print("Review questions you got wrong below:")
for w in result["wrong"]:
print(" -", w["question"], "Answer:", w["answer"])
if __name__ == "__main__":
main()
Explanation:
- We load questions from a JSON file.
- We shuffle them and present each question with numbered choices.
- We take user input, check if it matches the correct answer, track score.
- We save a simple history file (so you can see past performance).
- At the end we list which questions you got wrong (so you can review).
Adding Questions or Editing:
You might want a function to add questions without manually editing the JSON. Here’s a simple CLI prompt you can append:
def add_question():
questions = load_questions()
next_id = max(q["id"] for q in questions) + 1
q_text = input("Enter question: ")
choices = []
for i in range(4):
choices.append(input(f"Choice {i+1}: "))
answer = input("Enter correct choice exactly: ")
questions.append({
"id": next_id,
"question": q_text,
"choices": choices,
"answer": answer
})
with open(QUESTIONS_FILE, "w") as f:
json.dump(questions, f, indent=2)
print("Question added.")
# In main(), you might add:
print("Do you want to (1) take quiz or (2) add question?")
sel = input("Your choice: ")
if sel == "2":
add_question()
else:
result = quiz_user(questions)
...Why This Project Matters & Why It’s Better
Visual Friendly Learning:
Most “Walmart AML CBL answers” articles focus on what the training covers (definitions, regulations, red flags) but they don’t give you a way to practice or quiz yourself. Our tool flips that: it makes you active rather than passive. Active recall is proven to help retention.
Accessibility and Coding Literacy:
The competitor articles either assume you’re in a compliance role or deep in AML tech. Our tool uses very accessible Python. You don’t need to be a data scientist; just comfortable with basic scripts. If you aren’t comfortable yet, it’s a chance to learn Python too.
Specific to the Use Case:
We tie into the keyword: How to Build a “Walmart Anti Money Laundering CBL Answers” Tool with Python. None of the competitors teach the actual building of the tool. They discuss the training (Walmart’s AML CBL) or AML systems generally—but not building your own study-tool. So this is new.
Full Code Recap and Walk Through
Here’s a slightly cleaned-up version of the full tool. Save as quiz.py.
import json
import random
import os
from datetime import datetime
QUESTIONS_FILE = "questions.json"
HISTORY_FILE = "history.json"
def load_questions():
if not os.path.exists(QUESTIONS_FILE):
print(f"Questions file {QUESTIONS_FILE} not found. Please create it and add questions.")
exit(1)
with open(QUESTIONS_FILE, "r") as f:
return json.load(f)
def save_history(history):
with open(HISTORY_FILE, "w") as f:
json.dump(history, f, indent=2)
def load_history():
if os.path.exists(HISTORY_FILE):
with open(HISTORY_FILE, "r") as f:
return json.load(f)
else:
return {"sessions": []}
def quiz_user(questions):
correct = 0
wrong_list = []
random.shuffle(questions)
for q in questions:
print("\nQ:", q["question"])
choices = q["choices"].copy()
random.shuffle(choices)
for idx, choice in enumerate(choices, start=1):
print(f" {idx}. {choice}")
ans = input("Your answer (number): ")
try:
ans_idx = int(ans) - 1
if ans_idx < 0 or ans_idx >= len(choices):
raise ValueError
chosen = choices[ans_idx]
except:
print("Invalid choice. Skipping question.")
wrong_list.append(q)
continue
if chosen == q["answer"]:
print("Correct!")
correct += 1
else:
print("Wrong. Correct answer:", q["answer"])
wrong_list.append(q)
total = len(questions)
print(f"\nYour score: {correct} out of {total}")
return {"date": datetime.now().isoformat(), "correct": correct, "total": total, "wrong_ids": [w["id"] for w in wrong_list]}
def add_question():
questions = load_questions()
next_id = max(q["id"] for q in questions) + 1 if questions else 1
q_text = input("Enter question: ")
choices = []
for i in range(4):
choices.append(input(f"Choice {i+1}: "))
answer = input("Enter correct choice exactly as given above: ")
questions.append({
"id": next_id,
"question": q_text,
"choices": choices,
"answer": answer
})
with open(QUESTIONS_FILE, "w") as f:
json.dump(questions, f, indent=2)
print("Question added with ID", next_id)
def main():
print("=== Walmart AML CBL Study Tool ===")
print("1. Take quiz")
print("2. Add question")
sel = input("Choose option (1 or 2): ").strip()
if sel == "2":
add_question()
return
questions = load_questions()
history = load_history()
result = quiz_user(questions)
history["sessions"].append(result)
save_history(history)
print("\n=== Review incorrect questions ===")
wrong_ids = result["wrong_ids"]
if wrong_ids:
for q in questions:
if q["id"] in wrong_ids:
print(f" - QID {q['id']}: {q['question']} -> Answer: {q['answer']}")
else:
print("Great job—zero wrong!")
if __name__ == "__main__":
main()Tips & Best Practices
Keep Questions Realistic & Role-Relevant
Since this is about the Walmart AML CBL, tailor questions to the kinds of scenarios Walmart employees might face (retail store, money transfers, gift cards, returns). For example:
- “A customer pays cash for multiple high-value items, then uses returns to get a refund — what should you do?”
- “Under Walmart policy, when must you escalate a transaction to the AML compliance officer?”
Regularly Review Wrong Answers
Use the history file to identify which questions keep tripping you up. Create a “review set” of those questions and quiz yourself specifically on them until you’re consistently correct.
Expand Slowly
Start with maybe 10-20 questions. Once comfortable, expand to 50-100. If you build a community of learners, they can share questions (just ensure you’re not violating any proprietary training content or rules).
Version Control
If you’re comfortable, keep your project in a Git repository. That way you can track changes, pull in new features, or even turn this into a web-app later.
Conclusion
You now have a clear roadmap for building a useful study-tool: How to Build a “Walmart Anti Money Laundering CBL Answers” Tool with Python. You looked at what competitors currently offer (training reviews, conceptual articles, AML-tech blogs), saw where they fall short, and built something more actionable. You’ve got full code, idea of how to expand, tips on best practices, and warnings about pitfalls.