Exam AI-102 topic 3 question 12 discussion

My guess is A. An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, Address could have the following structure: Address: 4567 Main Street, NY, 98052, USA Building Number: 4567 Street Name: Main Street State: NY Zip Code: 98052 Country: USA

ML. Answer is A

Correct answer is D as per ChatGPT. Here is the response, "For capturing billing addresses in a Language Understanding model, the best choice would be Pattern.any (Option D). This is because billing addresses can vary greatly in format and content, and using Pattern.any allows for the flexibility needed to capture this variability effectively."

Selected Answer: A

I say this answer is A.

Copilot says Pattern.any The Pattern.any entity type is designed to capture free-form text, which makes it suitable for capturing billing addresses that can come in various formats. It uses pattern matching to predict and extract data.

I know you don't know what I'm talking about, but if you think as Crossroads leads you, the answer is A.

ML Entity with Structure An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, an Address entity could have the following structure: Address: 4567 Main Street, NY, 98052, USA Building Number: 4567 Street Name: Main Street State: NY Zip Code: 98052 Country: USA

A is right answer.

Given these options, A. Machine Learned is the most appropriate choice for capturing billing addresses. Billing addresses are complex entities with a lot of variability in their format and structure. A machine-learned entity is capable of understanding and extracting such complex information from natural language inputs, which makes it suitable for this purpose. It can learn from examples and capture the billing address as an entity based on the context in which it appears, which is essential for handling the wide range of ways in which addresses can be presented.

duplicated question https://learn.microsoft.com/en-us/azure/ai-services/LUIS/concepts/entities

ML Entity with Structure An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, an Address entity could have the following structure: Address: 4567 Main Street, NY, 98052, USA Building Number: 4567 Street Name: Main Street State: NY Zip Code: 98052 Country: USA

Correct answer is A

Same as Question 7. https://www.examtopics.com/discussions/microsoft/view/60239-exam-ai-102-topic-3-question-7-discussion

A is the answer. https://learn.microsoft.com/en-us/azure/cognitive-services/LUIS/concepts/entities#machine-learned-ml-entity Machine learned entity uses context to extract entities based on labeled examples. It is the preferred entity for building LUIS applications. It relies on machine-learning algorithms and requires labeling to be tailored to your application successfully. Use an ML entity to identify data that isn’t always well formatted but have the same meaning. An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, an Address entity could have the following structure: Address: 4567 Main Street, NY, 98052, USA Building Number: 4567 Street Name: Main Street State: NY Zip Code: 98052 Country: USA

C. geographyV2 The geographyV2 prebuilt entity in Language Understanding (LUIS) is designed to recognize and label entities that are geographical locations, such as city, state, or country. This would be suitable for capturing billing addresses in an e-commerce platform.

Wherever it is address it is ML