Try and practice the latest Microsoft : AI-102日本語 real questions & answers
Updated: Sep 02, 2026
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How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Microsoft AI-102日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (scaled score out of 1000) |
| Exam Duration: | 100 minutes |
| Available Languages: | Korean, Portuguese (Brazil), English, Italian, Indonesian, German, Spanish, Chinese (Simplified), Arabic (Saudi Arabia), Japanese, French |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure AI Fundamentals (AI-900) Microsoft Certified: Azure Developer Associate |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Exam Format: | Drag-and-drop, Hot area, Multiple select, Performance-based scenarios, Case studies, Multiple choice |
| Recommended Training: | AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Microsoft Learn Learning Paths for AI-102 |
| Exam Registration: | Pearson VUE Scheduling Microsoft Certification Exam Registration |
| Sample Questions: | Microsoft AI-102日本語 Sample Questions |
| Exam Way: | Online proctored (OnVUE) or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: experience with Azure services, AI concepts, and proficiency in Python or C#; AI-900 certification is recommended but not required |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102 |
Topics of AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates should apprehend the examination topics before they begin of preparation. because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:
1. Analyze solution requirements (25-30%)
Recommend Cognitive Services APIs to meet business requirements
- Select the appropriate data processing technologies
- Select the appropriate AI models and services
- Identify components and technologies required to connect service endpoints
- Select the processing architecture for a solution
- Identify automation requirements
Map security requirements to tools, technologies, and processes
- Identify which users and groups have access to information and interfaces
- Identify appropriate tools for a solution
- Identify auditing requirements
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
Select the software, services, and storage required to support a solution
- Identify integration points with other Microsoft services
- Identify storage required to store logging, bot state data, and Cognitive Services output
- Identify appropriate services and tools for a solution
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Define an AI application workflow process
- Design the integration point between multiple workflows and pipelines
- Design pipelines that use AI apps
- Select an AI solution that meet cost constraints
- Design a strategy for ingest and egress data
- Design pipelines that call Azure Machine Learning models
Design solutions that uses Cognitive Services
- Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs
Design solutions that implement the Bot Framework
- Design bot services that use Language Understanding (LUIS)
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
- Integrate bots and AI solutions
Design the compute infrastructure to support a solution
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Select a compute solution that meets cost constraints
Design for data governance, compliance, integrity, and security
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure that data adheres to compliance requirements defined by your organization
- Ensure appropriate governance of data
- Define how users and applications will authenticate to AI services
- Design a content moderation strategy for data usage within an AI solution
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Manage the flow of data through the solution components
- Implement data logging processes
- Develop AI pipelines
- Create solution endpoints
- Develop streaming solutions
- Define and construct interfaces for custom AI services
Integrate AI services with solution components
- Configure prerequisite components to allow connectivity to the Bot Framework
- Implement Azure Search in a solution
- Configure integration with Cognitive Services
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
Monitor and evaluate the AI environment
- Monitor AI components for availability
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Recommend changes to an AI solution based on performance data
- Maintain an AI solution for continuous improvement
- Identify the differences between expected and actual workflow throughput
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
| 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 an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Integrate vision capabilities into applications - Process and index video content - Extract text and handwriting from images - Analyze images and detect objects/features |
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