サンプル問題 1： 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. (Choose three.)
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.
正解：A, B, F
Explanation: 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
サンプル問題 2： 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?
A: Dispatch
B: chatdown
C: Language Understanding
D: Microsoft Translator
正解：A
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
サンプル問題 3： 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.
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/v3.2/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. 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
Microsoft AI-102試験準備をしていますか？それとも自分は全部できそうです。復習する必要がないと思いますか？それとも自分は何もできないような気がします。どうすればいいでしょうか？実は、この問題を解決するのは難しくないです。勉強者がやるべきのは分解分析をマスターすることです。ここではAI-102問題集を使って学習中の課題を確認し、知識を整理してDesigning and Implementing a Microsoft Azure AI Solution試験の準備を進めます。
AI-102問題集を利用して学習中の問題を探し出す。
Microsoft AI-102試験のすべての試験資料ポイントを勉強した後、AI-102問題集の中の問題点を練習してみてください。重要な試験点はどこにあるか、つまりMicrosoftが把握してほしいものは何ですか？そして練習中の問題点を整理して分析してください。できない問題に対して、正しい答えの考えをはっきりさせたいです。それに関する知識点を明らかにします。間違った問題に対しては、具体的に自分がどこで間違っているかを見に行きます。このステップで使う試験資料ポイントは何ですか？間違った問題をこのように一歩分解して分析すると、自然に自分の復習する方向を見つけます。
その次に、非常規の試験ポイントのテーマは多すぎてこだわる必要はなくて、重要な試験点のテーマはきっと知識の点をわかります。特にDesigning and Implementing a Microsoft Azure AI Solution試験は試験資料ポイントが多いです。一つの問題は知識ポイントだけではなく、復習する時はまとめをマスターして、記憶を分類します。知識ポイントが覚えていないか、間違えていないかのために点数をなくしてはいけません。
