Google Cloud Professional Machine Learning Engineer — Question 254

You work for a company that is developing an application to help users with meal planning. You want to use machine learning to scan a corpus of recipes and extract each ingredient (e.g., carrot, rice, pasta) and each kitchen cookware (e.g., bowl, pot, spoon) mentioned. Each recipe is saved in an unstructured text file. What should you do?

Answer options

• A. Create a text dataset on Vertex AI for entity extraction Create two entities called “ingredient” and “cookware”, and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.
• B. Create a multi-label text classification dataset on Vertex AI. Create a test dataset, and label each recipe that corresponds to its ingredients and cookware. Train a multi-class classification model. Evaluate the model’s performance on a holdout dataset.
• C. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.
• D. Create a text dataset on Vertex AI for entity extraction. Create as many entities as there are different ingredients and cookware. Train an AutoML entity extraction model to extract those entities. Evaluate the model’s performance on a holdout dataset.

Correct answer: A

Explanation

Option A is correct because it focuses on creating a dataset for entity extraction specifically for ingredients and cookware, which is the primary requirement of the task. Option B is incorrect as multi-label classification does not suit the need for entity extraction. Option C is not ideal since it relies on the Natural Language API without the structured approach of training a custom model. Option D overcomplicates the task by creating an excessive number of entities instead of focusing on the two categories needed.