L1 regularization it's good for feature selection
https://www.quora.com/How-does-the-L1-regularization-method-help-in-feature-selection
https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

The shape of the spot is quite important for the severity of the disease, and image segmentation could help us to determine it in a more granular manner. And it is often used in the healthcare industry, for getting the shapes of all the cancerous cells.

While Google Data Studio (Option B) is a powerful data visualization and reporting tool, it might not provide the same level of flexibility and sophistication for statistical analyses compared to a notebook environment.

A is not correct because the test does not check that the model has enough parameters to learn the task.
B is not correct because the loss should decrease if you have enough parameters to learn the task.
C is not correct because outperforming the linear model does not guarantee that the model has enough parameters to learn tasks with non-linear data representations. The option also doesn't quantify a metric to give an indication of how well the model performed.
D is correct because the test can check that the model has enough parameters to memorize the task.
https://developers.google.com/machine-learning/testing-debugging/pipeline/deploying#testing-for-algorithmic-correctness

A is incorrect because you should not automate the higher value requests.
B is incorrect because live agents are better suited to handle these complicated requests.
C is correct because it enables a machine to handle the most simple requests and gives the live agents more opportunity to handle higher value requests.
D is incorrect because Dialogflow can handle the same word in multiple intents.
https://cloud.google.com/blog/products/ai-machine-learning/discover-card-how-we-designed-an-experiment-to-evaluate-conversational-experience-platforms