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How to study the AI-102: Designing and Implementing an Azure AI Solution Exam
Preparation of certification exams could be covered with two resource types . The first one are the study guides, reference books and study forums that are elaborated and appropriate for building information from ground up. Apart from them video tutorials and lectures are a good option to ease the pain of through study and are relatively make the study process more interesting nonetheless these demand time and concentration from the learner. Smart candidates who wish to create a solid foundation altogether examination topics and connected technologies typically mix video lectures with study guides to reap the advantages of each but practice exams or practice exam engines is one important study tool which goes typically unnoted by most candidates. Practice exams are designed with our experts to make exam prospects test their knowledge on skills attained in course, as well as prospects become comfortable and familiar with the real exam environment. Statistics have indicated exam anxiety plays much bigger role of students failure in exam than the fear of the unknown. DumpTorrent expert team recommends preparing some notes on these topics along with it don't forget to practice AI-102 exam dumps which had been written by our expert team, each of these can assist you loads to clear this exam with excellent marks.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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 AI models and services
- Identify automation requirements
- 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 auditing requirements
- Identify appropriate tools for a solution
- Identify which users and groups have access to information and interfaces
- 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 appropriate services and tools for a solution
- Identify integration points with other Microsoft services
- Identify storage required to store logging, bot state data, and Cognitive Services output
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Design a strategy for ingest and egress data
- Design pipelines that call Azure Machine Learning models
- Design pipelines that use AI apps
- Select an AI solution that meet cost constraints
- Design the integration point between multiple workflows and pipelines
- Define an AI application workflow process
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
- Integrate bots and AI solutions
- Design bot services that use Language Understanding (LUIS)
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
Design the compute infrastructure to support a solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Select a compute solution that meets cost constraints
Design for data governance, compliance, integrity, and security
- Design a content moderation strategy for data usage within an AI solution
- Ensure that data adheres to compliance requirements defined by your organization
- Define how users and applications will authenticate to AI services
- Ensure appropriate governance of data
- 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
- Implement data logging processes
- Define and construct interfaces for custom AI services
- Develop streaming solutions
- Develop AI pipelines
- Manage the flow of data through the solution components
- Create solution endpoints
Integrate AI services with solution components
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Configure prerequisite components to allow connectivity to the Bot Framework
- Implement Azure Search in a solution
- Configure integration with Cognitive Services
Monitor and evaluate the AI environment
- Recommend changes to an AI solution based on performance data
- Monitor AI components for availability
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Identify the differences between expected and actual workflow throughput
- Maintain an AI solution for continuous improvement
Flexible version
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