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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.
- This is beta exam.
- Length of Examination: 50 mins
- 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.)
- Language: English, Japanese, Chinese (Simplified), Korean.
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
- Identify automation requirements
- Select the appropriate AI models and services
- Select the processing architecture for a solution
- Identify components and technologies required to connect service endpoints
- Select the appropriate data processing technologies
Map security requirements to tools, technologies, and processes
- Identify which users and groups have access to information and interfaces
- Identify auditing requirements
- Identify appropriate tools for a solution
- 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 storage required to store logging, bot state data, and Cognitive Services output
- Identify appropriate services and tools for a solution
- Identify integration points with other Microsoft services
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Define an AI application workflow process
- Design a strategy for ingest and egress data
- Design pipelines that use AI apps
- Design the integration point between multiple workflows and pipelines
- Select an AI solution that meet cost constraints
- 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)
- Design bots that integrate with channels
- Integrate bots and AI solutions
- Integrate bots with Azure app services and Azure Application Insights
Design the compute infrastructure to support a solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Select a compute solution that meets cost constraints
- Identify whether to create a GPU, FPGA, or CPU-based solution
Design for data governance, compliance, integrity, and security
- Ensure that data adheres to compliance requirements defined by your organization
- Ensure appropriate governance of data
- Design a content moderation strategy for data usage within an AI solution
- Define how users and applications will authenticate to AI services
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Develop AI pipelines
- Define and construct interfaces for custom AI services
- Create solution endpoints
- Implement data logging processes
- Manage the flow of data through the solution components
- Develop streaming solutions
Integrate AI services with solution components
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Configure integration with Cognitive Services
- Implement Azure Search in a solution
- Configure prerequisite components to allow connectivity to the Bot Framework
Monitor and evaluate the AI environment
- Maintain an AI solution for continuous improvement
- Monitor AI components for availability
- Identify the differences between expected and actual workflow throughput
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Recommend changes to an AI solution based on performance data
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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!
How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?
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