You are developing the knowledgebase. You use Azure Video Analyzer for Media (previously Video indexer) to obtain transcripts of webinars. You need to ensure that the solution meets the knowledgebase requirements. What should you do?
A. Create a custom language model
B. Configure audio indexing for videos only
C. Enable multi-language detection for videos
D. Build a custom Person model for webinar presenters
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
Question 232
HOTSPOT - You are planning the product creation project. You need to build the REST endpoint to create the multilingual product descriptions. How should you complete the URI? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
Box 1: api.cognitive.microsofttranslator.com Translator 3.0: Translate. Send a POST request to: https://api.cognitive.microsofttranslator.com/translate?api-version=3.0 Box 2: /translate - Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate
Question 233
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 Form 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?
A. documentResults.docType == "prebuilt:receipt"
B. documentResults.fields.*.confidence < 0.7
C. documentResults.fields.ReceiptType.confidence > 0.7
D. documentResults.fields.MerchantName.confidence < 0.7
Need to specify the field name, and then use < 0.7 to handle trigger if confidence score is less than 70%. Scenario: - AI solution responses must have a confidence score that is equal to or greater than 70 percent. - When the response confidence score of an AI response is lower than 70 percent the response must be improved by human input. Reference: https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/api-v2-0/reference-sdk-api-v2-0
Question 234
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. NOTE: Each correct selection is worth one point.
A. Make API queries to the autocomplete endpoint and include suggesterName in the body.
B. Add a suggester that has the three product name fields as source fields.
C. Make API queries to the search endpoint and include the product name fields in the searchFields query parameter.
D. Add a suggester for each of the three product name fields.
E. Set the searchAnalyzer property for the three product name variants.
F. Set the analyzer property for the three product name variants.
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. F. Use the default standard Lucene analyzer ("analyzer": null) or a language analyzer (for example, "analyzer": "en.Microsoft") on the field. Reference: https://docs.microsoft.com/en-us/azure/search/index-add-suggesters
Question 235
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.
A. /vision/v3.1/read/analyzeResults
B. /formrecognizer/v2.0/custom/models/{modelId}/analyze
C. /formrecognizer/v2.0/prebuilt/receipt/analyze
D. /vision/v3.1/describe
E. /vision/v3.1/read/analyze
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
Question 236
HOTSPOT - You are developing the knowledgebase by using Azure Cognitive Search. You need to build a skill that will be used by indexers. How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
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
Question 237
You are developing the knowledgebase by using Azure Cognitive Search. You need to process wiki content to meet the technical requirements. What should you include in the solution?
A. an indexer for Azure Blob storage attached to a skillset that contains the language detection skill and the text translation skill
B. an indexer for Azure Blob storage attached to a skillset that contains the language detection skill
C. an indexer for Azure Cosmos DB attached to a skillset that contains the document extraction skill and the text translation skill
D. an indexer for Azure Cosmos DB attached to a skillset that contains the language detection skill and the text translation skill
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
Question 238
You are developing the knowledgebase by using Azure Cognitive Search. You need to meet the knowledgebase requirements for searching equivalent terms. What should you include in the solution?
A. synonym map
B. a suggester
C. a custom analyzer
D. a built-in key phrase extraction skill
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
Question 239
HOTSPOT - You are developing the shopping on-the-go project. You need to build the Adaptive Card for the chatbot. How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
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]
Question 240
HOTSPOT - You are developing the shopping on-the-go project. You are configuring access to the QnA Maker (classic) resources. Which role should you assign to AllUsers and LeadershipTeam? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
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
