Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What is the main goal when training a machine learning model?

1. To minimize the complexity of the model

2. To make accurate predictions on unseen data

3. To maximize the training speed

4. To ensure the model can explain its decisions

The correct answer is: To make accurate predictions on unseen data

The primary objective of training a machine learning model is to enhance its ability to make accurate predictions on unseen data. This goal is central to the purpose of machine learning, which is to develop models that can generalize from the data they have been trained on to new, previously unseen instances. Achieving high accuracy on unseen data indicates that the model has learned the underlying patterns and relationships within the training data, allowing it to perform well in real-world scenarios where it encounters new inputs. While minimizing the complexity of the model, maximizing training speed, and ensuring the model can explain its decisions are important considerations in the development of machine learning systems, they do not capture the main goal of training. Reducing complexity can help prevent overfitting, and improving training speed is beneficial for efficiency, but the ultimate aim remains to create a model that can make reliable and accurate predictions when it's deployed in practice. Similarly, interpretability can enhance trust and understanding of the model's decisions but is secondary to the fundamental requirement of making accurate predictions on new data.