Answer:

Explanation:
Scenario: All videos must have transcripts that are associated to the video and included in product descriptions. Product
descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Step 1: Upload the video to blob storage
Given a video or audio file, the file is first dropped into a Blob Storage. T
Step 2: Index the video by using the Video Indexer API.
When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The
insights include: transcripts, OCRs, faces, topics, blocks, etc.
Step 3: Extract the transcript from the Video Indexer API.
Step 4: Translate the transcript by using the Translator API.
Reference: https://azure.microsoft.com/en-us/blog/get-video-insights-in-even-more-languages/
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-output-json-v2 Implement Natural
Language Processing Solutions

Answer:

Explanation:
Reference: https://github.com/Azure-Samples/cognitive-services-dotnet-sdk-samples/blob/master/documentation-
samples/quickstarts/ComputerVision/Program.cs

Answer:

Explanation:
Step 1: Create a new project and load sample documents
Create a new project. Projects store your configurations and settings.
Step 2: Label the sample documents
When you create or open a project, the main tag editor window opens.
Step 3: Train a custom model. Finally, train a custom model.
Reference: https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/label-tool

Answer:

B

Explanation:
Can search content in different formats, including video
Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.
Keywords extraction: Extracts keywords from speech and visual text.
Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language
processing (NLP).
Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.
Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.
Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.
Incorrect Answers:
C: Webinars Videos are in English.
Reference: https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-
overview

Answer:

C E

Explanation:
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/v3.1/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.
Reference: https://westus2.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-
preview/operations/GetAnalyzeReceiptResult https://docs.microsoft.com/en-us/rest/api/computervision/3.1/read/read

Answer:

Explanation:
Box 1: "categories": ["Locations", "Persons", "Organizations"], Locations, Persons, Organizations are in the outputs.
Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property Note: The categories
parameter is an array of categories that should be extracted. Possible category types: "Person", "Location", "Organization",
"Quantity", "Datetime", "URL", "Email". If no category is provided, all types are returned.
Box 2: {"name": " entities"}
The include wikis, so should include entities in the outputs.
Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the
following fields name (the actual entity name. This represents a "normalized" form) wikipediaId
wikipediaLanguage
wikipediaUrl (a link to Wikipedia page for the entity) etc.
Reference: https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-entity-recognition

Answer:

C

Explanation:
The wiki contains text in English, French and Portuguese.
Scenario: All planned projects must support English, French, and Portuguese.
The Document Extraction skill extracts content from a file within the enrichment pipeline. This allows you to take advantage
of the document extraction step that normally happens before the skillset execution with files that may be generated by other
skills.
Note: The Translator Text API will be used to determine the from language. The Language detection skill is not required.
Incorrect Answers:
Not A, not B: The wiki is stored in Azure Cosmos DB.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-document-extraction https://docs.microsoft.com/en-
us/azure/search/cognitive-search-skill-text-translation

Answer:

A

Explanation:
Within a search service, synonym maps are a global resource that associate equivalent terms, expanding the scope of a
query without the user having to actually provide the term. For example, assuming "dog", "canine", and "puppy" are mapped
synonyms, a query on "canine" will match on a document containing "dog".
Create synonyms: A synonym map is an asset that can be created once and used by many indexes.
Reference: https://docs.microsoft.com/en-us/azure/search/search-synonyms

Answer:

Explanation:
Box 1: name [language]
Chatbot must support interactions in English, Spanish, and Portuguese.
Box 2: "$when:${stockLevel != 'OK'}"
Product displays must include images and warnings when stock levels are low or out of stock.
Box 3: image.altText[language]

Answer:

Explanation:
Box 1: QnA Maker Editor
Scenario: Provide all employees with the ability to edit Q&As.
The QnA Maker Editor (read/write) has the following permissions:
Create KB API

Update KB API

Replace KB API

Replace Alterations

"Train API" [in new service model v5]

Box 2: Contributor
Scenario: Only senior managers must be able to publish updates. Contributor permission: All except ability to add new
members to roles
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/reference-role-based-access-control

Answer:

Explanation:
Box 1: name.en
Box 2: "$when": "${stockLevel != 'OK'}"
Product displays must include images and warnings when stock levels are low or out of stock.
Box 3:image.altText.en

Answer:

C

Explanation:
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.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/best-practices

Answer:

C

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch

Answer:

Explanation:
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
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api

Answer:

Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
1. Sign on to the LUIS portal.
2. Select the app in the list.
3. Select Manage in the app's 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.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto