You are building a Language Understanding model for an e-commerce platform.
You need to construct an entity to capture billing addresses.
Which entity type should you use for the billing address?
Exam AI-102 All QuestionsBrowse all questions from this exam
Question 119
Correct Answer: A
B
A regular expression entity extracts an entity based on a regular expression pattern you provide. It ignores case and ignores cultural variant. Regular expression is best for structured text or a predefined sequence of alphanumeric values that are expected in a certain format. For example:
Incorrect Answers:
C: The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents. GeographyV2 entity is supported in English culture.
The geographical locations have subtypes:
D: Pattern.any is a variable-length placeholder used only in a pattern's template utterance to mark where the entity begins and ends.
E: A list entity represents a fixed, closed set of related words along with their synonyms. You can use list entities to recognize multiple synonyms or variations and extract a normalized output for them. Use the recommend option to see suggestions for new words based on the current list.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types
Discussion
ExamPrep2021Option: A
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
MarceloManhaes
I agree it is clear that is ML entity, the sample above is on the URL https://learn.microsoft.com/en-us/azure/ai-services/LUIS/concepts/entities
LKLK10Option: A
ML. Answer is A
azurelearner666
Right! (the correct response is A, Machine Learned) See https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types It is a Machine Learned Entity (check ML Entity with Structure in the link, as it is an Address example… )
ap1234paOption: A
Wherever it is address it is ML
David_mlOption: A
A is correct
omankomanOption: 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
EliteAllenOption: C
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.
M25
https://learn.microsoft.com/en-us/azure/ai-services/luis/luis-reference-prebuilt-geographyv2?tabs=V3 The prebuilt geographyV2 entity detects places. The geographical locations have subtypes: poi point of interest city name of city countryRegion name of country or region continent name of continent state name of state or province I guess you could charge a bill for the Statue of Liberty on Ellis Island as a (fixed) “poi”, but a more generalized rule would rather look for an Address entity with sub-entities (variable) as an ML Entity with Structure type
krzkrzkraOption: A
Selected Answer: A
HaraTadahisaOption: A
I say this answer is A.
etellezOption: D
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.
reigenchimpoOption: A
I know you don't know what I'm talking about, but if you think as Crossroads leads you, the answer is A.
nanaw770Option: A
A is right answer.
evangelistOption: A
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.
rdemontisOption: A
duplicated question https://learn.microsoft.com/en-us/azure/ai-services/LUIS/concepts/entities
jakespeedOption: 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
ExamDevOption: A
Correct answer is A
zellck
Same as Question 7. https://www.examtopics.com/discussions/microsoft/view/60239-exam-ai-102-topic-3-question-7-discussion
zellckOption: A
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