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You are developing the smart e-commerce project.

You need to implement autocompletion as part of the Cognitive Search solution.

Which three actions should you perform? Each correct answer presents part of the solution. (Choose three.)

NOTE: Each correct selection is worth one point.

Answer: A, B, F, F

See the explanation below.

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search&api-version=2020-06-30


'search': 'minecraf',

'suggesterName': 'sg'


B: In Azure Cognitive Search, typeahead or 'search-as-you-type' is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for 'Seattle' will have prefix combinations of 'sea', 'seat', 'seatt', and 'seattl' to support typeahead.



You need to develop an extract solution for the receipt images. The solution must meet the document processing requirements and the technical requirements.

You upload the receipt images to the From Recognizer API for analysis, and the API returns the following JSON.


Which expression should you use to trigger a manual review of the extracted information by a member of the Consultant-Bookkeeper group?

Answer: C

See the explanation below.

Need to specify the field name, and then use < 0.7 to handle trigger if confidence score is less than 70%.



You are developing the chatbot.

You create the following components:

* A QnA Maker resource

* A chatbot by using the Azure Bot Framework SDK

You need to add an additional component to meet the technical requirements and the chatbot requirements. What should you add?

Answer: A

See the explanation below.

Scenario: All planned projects must support English, French, and Portuguese.

If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use the Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.



You are developing the document processing workflow.

You need to identify which API endpoints to use to extract text from the financial documents. The solution must meet the document processing requirements.

Which two API endpoints should you identify? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Answer: B, C

See the explanation below.

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.




You are developing the chatbot.

You create the following components:

* A QnA Maker resource

* A chatbot by using the Azure Bot Framework SDK.

You need to integrate the components to meet the chatbot requirements.

Which property should you use?

Answer: D

See the explanation below.

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or 'None' is returned and the default response is 'No good match found in the KB'. You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.


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