DRAG DROP -
You have 100 chatbots that each has its own Language Understanding model.
Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Box 1: AddPhraseListAsync -
Example: Add phraselist feature -
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
EnabledForAllModels = false,
IsExchangeable = true,
Name = "QuantityPhraselist",
Phrases = "few,more,extra"
Box 2: PhraselistCreateObject -
DRAG DROP -
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. (Choose three.)
Select and Place:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app's package from LUIS portal
The versioned app's package is available from the Versions list page.
1. Sign on to the LUIS portal.
2. Select the app in the list.
3. Select Manage in the app's navigation bar.
4. Select Versions in the left navigation bar.
5. Select the checkbox to the left of the version name in the list.
6. Select the Export item from the contextual toolbar above the list.
7. Select Export for container (GZIP).
8. The package is downloaded from the browser.
Step 2: Select v1.1 of app1.
A trained or published app packaged as a mounted input to the container with its associated App ID.
Step 3: Run a contain and mount the model file.
Run the container, with the required input mount and billing settings.
You need to build a chatbot that meets the following requirements:
✑ Supports chit-chat, knowledge base, and multilingual models
✑ Performs sentiment analysis on user messages
✑ Selects the best language model automatically
What should you integrate into the chatbot?
Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.
QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information.
Text Analytics: Mine insights in unstructured text using natural language processing (NLP)ג€"no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input
A, B, D: Dispatch uses sample utterances for each of your botג€™s different tasks (LUIS, QnA Maker, or custom), and builds a model that can be used to properly route your userג€™s request to the right task, even across multiple bots.
Your company wants to reduce how long it takes for employees to log receipts in expense reports. All the receipts are in English.
You need to extract top-level information from the receipts, such as the vendor and the transaction total. The solution must minimize development effort.
Which Azure Cognitive Services service should you use?
Azure Form Recognizer is a cognitive service that lets you build automated data processing software using machine learning technology. Identify and extract text, key/value pairs, selection marks, tables, and structure from your documentsג€"the service outputs structured data that includes the relationships in the original file, bounding boxes, confidence and more.
Form Recognizer is composed of custom document processing models, prebuilt models for invoices, receipts, IDs and business cards, and the layout model.