Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.

Option D (Correct): 'Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas': This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.

Option A: 'Store the data in Amazon S3 and use SageMaker built-in algorithms' is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.

Option B: 'Import the data into Amazon SageMaker Data Wrangler' is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.

Option C: 'Use Amazon Personalize Trending-Now recipe' is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.

AWS AI Practitioner Reference:

Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.