Google Cloud Digital Leader Practice Exam

Artificial intelligence is best suited for replacing or simplifying rule-based systems. Which is an example of this in action?

1. Implementing AI to develop a new product or service that has never been seen before.

2. Using AI to replace a human decision-maker in complex situations, such as those involving life-or-death choices.

3. Using a reinforcement learning algorithm to train autonomous drones for package delivery.

4. Training a machine learning model to predict a search result ranking.

The correct answer is: Training a machine learning model to predict a search result ranking.

Using a machine learning model to predict a search result ranking exemplifies how artificial intelligence can enhance or simplify traditional rule-based systems. In many traditional search engines, ranking results involved predefined rules based on keyword matching and static criteria. However, incorporating machine learning enables the system to analyze vast amounts of data, learning from user interactions and preferences to refine its ranking algorithm dynamically. This application of AI allows for more nuanced and effective search results that adapt over time, as the model can continually improve its predictions based on new data. This stands in contrast to static rule-based systems that could become outdated or unable to handle complex queries effectively. The ability of the AI model to learn and adjust offers a significant advancement over simpler, hard-coded search ranking methodologies. In other options, while they also demonstrate the capabilities of AI, they do not represent the direct simplification or replacement of rule-based systems as clearly as predicting search rankings does. For example, developing a completely new product (first choice) involves innovation and ideation beyond mere simplification. Replacing a human decision-maker in complex situations (second choice) touches on decision-making challenges rather than rule-based adaptation. Training drones for package delivery involves a different aspect of AI, focusing on operational tasks rather than transforming an existing rule-based