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For more info read reference:
microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
Prep Options to Choose
The preparation process for the exam is a vital step on your way of passing the test, that’s why you need to find the most actual and reliable resources. Microsoft offers two options for you to choose from online free training or instructor-led, which is a paid one. For example, the free training represents a collection of learning paths each of which contains the different number of modules, from one to five. Thus, you can choose which ones to follow: Prepare for AI engineering (1 module), Process and Translate Speech with Azure Cognitive Speech Services (2 modules), Create computer vision solutions with Azure Cognitive Services (3 modules), to name a few. The paid course is known to be Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution and lasts for 4 days. It is intended for software developers interested in developing skills to build AI infused apps that use Azure Cognitive Search and Services, and Microsoft Bot Framework.
In addition, you can check the Amazon website to find the books on the topics included in the exam to ace it from the first attempt. Only after the successful passing the AI-102 exam you will earn the Microsoft Certified: Azure AI Engineer Associate certification. So, good luck!
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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Type of Questions: This test format is multiple choice.
- Length of Examination: 50 mins
- This is beta exam.
- Language: English, Japanese, Chinese (Simplified), Korean.
- Passing Score: 700 / 1000
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Select suitable AI models |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Implement computer vision solutions | 10-15% | - Process and index video content - Build and deploy custom vision models - Analyze images and detect objects/features - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
Azure Al Speech サービスとカスタム ニューラル音声を使用する電話通話処理ソリューションを構築しています。
カスタム音声モデルを作成する必要があります。
Speech Studio から順番に実行する必要がある 5 つのアクションはどれですか。回答するには、適切なアクションをアクション リストから回答領域に移動し、正しい順序に並べます。
Correct Answer:
Explanation:
Comprehensive Detailed Explanation
A custom neural voice (professional voice) project in Azure AI Speech requires that you follow Responsible AI and consent steps before any model training. The correct workflow in Speech Studio / Azure AI Foundry is:
Create a custom voice project - This is the container that will hold the voice talent consent, your training datasets, models, and deployments. Each project is scoped to a locale and other attributes. Microsoft Learn Collect and upload consent - Microsoft requires explicit authorization from the voice talent. In practice, you add the voice talent consent to the project. The platform requires the verbal consent recording (WAV); many guidance and exam items also include the signed consent document (PDF) as part of consent capture to meet Responsible AI expectations. The consent step is performed before uploading training data.
Upload the voice talent's verbal consent as a WAV file - The consent audio verifies that the person who granted consent is the same as the speaker in your training data. The documentation shows consent is a recorded statement uploaded to the project. Microsoft Learn Analyze the quality of the audio data and fix issues - After data upload, Speech Studio runs validation and provides a per-utterance analysis (format checks, sampling rate, script match, pronunciation/noise scoring).
You must resolve issues and run Analyze data before the dataset can be used for training. Microsoft Learn Train the model using a neural training method - Start model training from the project and choose Neural as the training type for custom neural voice. Microsoft Learn Why not "upload speech samples as WMA/MP3 files"?
For the standard, recommended Individual utterances + matching transcript data type used to fine-tune a professional neural voice, the audio files must be WAV/RIFF PCM; the docs explicitly reference ".wav" throughout data checks and corrections. WMA isn't supported, and MP3 isn't accepted for this core data type.
(Long audio / audio-only workflows can accept MP3 for preprocessing, but the canonical training dataset for custom neural voice is WAV.) Microsoft Azure AI References Create a project for professional voice (Speech Studio/Azure AI Foundry): Project contains consent, datasets, models, endpoints. Microsoft Learn Add voice talent consent to the professional voice project: Upload the recorded consent statement for the voice talent. Microsoft Learn Professional voice fine-tuning data - required formats: Individual utterances + transcript require WAV/RIFF PCM; guidance on sample rate and transcript format. Microsoft Learn Add a professional voice training dataset & resolve data issues: Data validation, pronunciation/noise checks, and the Analyze data step prior to training. Microsoft Le arn Train your professional voice model: Start training and select Neural method. Microsoft Learn
Azure OpenA1 リソースを含む Azure サブスクリプションがあり、以下の設定を持つモデルを構成します。
* 温度: 1
* 最高確率: 0.5
* 最大レスポンストークン数: 100
モデルに質問すると、次の応答が返されます。
以下の各文について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。
注意: 正しい選択ごとにポイントが加算されます。
Correct Answer:
Explanation:
The subscription will be charged 86 tokens for the execution of the session. No The text completion was truncated because the Max response tokens value was exceeded. No The prompt_tokens value will be included in the calculation of the Max response tokens value. No From the response payload:
usage.prompt_tokens = 37
usage.completion_tokens = 86
usage.total_tokens = 123
finish_reason = " stop "
Billing is for total tokens (input + output), not just completion tokens.
Azure OpenAI bills on both prompt (input) and completion (output) tokens. Here, the total is 123 tokens, so it is not just 86. Hence No.
Not truncated by max response tokens.
If a response hits the maximum allowed output tokens, finish_reason would be " length " . Here it is " stop " and the model produced only 86 completion tokens while Max response tokens was set to 100, so the limit was not reached. Hence No.
max response tokens applies only to the generated output, not the prompt.
max_tokens (Max response tokens) limits the number of tokens the model may generate in the completion; it does not include prompt_tokens . Hence No.
Microsoft References
Azure OpenAI Service - Chat Completions API (parameters, max_tokens , response usage , finish_reason ).
https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions Azure OpenAI pricing and token usage model (charges for input and output tokens).
https://learn.microsoft.com/azure/ai-services/openai/overview#pricing
Token counting and limits (context vs. max output tokens).
https://learn.microsoft.com/azure/ai-services/openai/concepts/tokenizers#token-limits
あなたは、Foundry Tools の Azure Document Intelligence を使用してスキャンされたドキュメントから以下のデータを抽出する App1 という名前のアプリを構築しています。
* お届け先の住所
* 請求先住所
* 顧客ID
* 支払金額
* 期日
* 合計税額
* 小計
App1に使用するモデルを特定する必要があります。ソリューションは開発作業を最小限に抑える必要があります。どのモデルを使用すべきでしょうか？
- A. 請求書
- B. 一般文書
- C. カスタム抽出モデル
- D. 契約
Correct Answer: A 🗳️
データウェアハウスの主な目的は何ですか？
- A. ソースデータストアとターゲットデータストア間の変換サービスを提供する
- B. トランザクション基幹業務（LOB）アプリケーションにストレージを提供する
- C. リレーショナルおよび非リレーショナルの履歴データの読み取り専用ストレージを提供する
- D. 複数のソースからのデータに依存する複雑なクエリへの回答を提供する
Correct Answer: D 🗳️
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Azure サブスクリプションをお持ちです。このサブスクリプションには、Model1 という GPT-3.5 Turbo モデルをホストする Azure OpenAI リソースが含まれています。
Model1 を次のシステムメッセージを使用するように設定します。「あなたは、人々が数学パズルを解くのを手伝う AI アシスタントです。4 歳児からのリクエストであるかのように、答えを説明してください。」これはどのような種類のプロンプトエンジニアリング手法の例ですか？
- A. 思考の連鎖
- B. 少数ショット学習
- C. プライミング
- D. アフォーダンス
Correct Answer: C 🗳️
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