Question

What does the phrase “garbage in, garbage out” mean in the context of an AI project?

• A. If the data collected is bad, the AI model will not be effective.
• B. If you select the wrong AI model, the project will fail.
• C. AI algorithms can correct bad data inputs automatically.
• D. Garbage data should be avoided in the deployment phase.

Hint:

Tip: Better data → better model output. Always ensure data is clean, relevant, and high-quality.

Answer 1

The Correct Option is A

The phrase “garbage in, garbage out” is a popular saying in data science and AI fields.
It means that if you feed poor-quality or irrelevant data into an AI system, the output will also be poor.
AI models learn patterns from the data they are given, so inaccurate, incomplete, or biased data leads to flawed predictions and results.
AI systems cannot automatically fix fundamentally bad data during training — good preprocessing is essential.
Choosing the right AI model is important too, but this phrase specifically focuses on the data quality, not the model selection.
Avoiding garbage data only during deployment is not enough — the problem starts at data collection and preparation.
Therefore, if the data collected is bad, the AI model will not perform well — this is what “garbage in, garbage out” means.
So, the correct answer is option (A).