Design results that uses Cognitive Services
- Design results that use vision, speech, language, knowledge, hunt, and anomaly discovery APIs
- Integrate bots and AI results
- Design bot services that use Language Understanding (LUIS)
- Design bots that integrate with channels
- Integrate bots with Azure app services and Azure operation perceptivity
Design the cipher structure to support a result
- Identify whether to produce a GPU, FPGA, or CPU- grounded result
- Identify whether to use a pall- grounded, on- demesne, or mongrel cipher structure
- elect a cipher result that meets cost constraints
Design for data governance, compliance, integrity, and security
- Define how druggies and operations will authenticate to AI services
- Design a content temperance strategy for data operation within an AI result
- insure that data adheres to compliance conditions defined by your association
- insure applicable governance of data
- Design strategies to insure that the result meets data sequestration regulations and assiduity norms
3. Apply and cover AI results (25- 30)
Apply an AI workflow
- Develop AI channels
- Manage the inflow of data through the result factors
- apply data logging processes
- Define and construct interfaces for custom AI services
- produce result endpoints
- Develop streaming results
Integrate AI services with result factors
- Configure prerequisite factors and input datasets to allow the consumption of Cognitive Services APIs
- Configure integration with Cognitive Services
- Configure prerequisite factors to allow connectivity to the Bot Framework
- apply Azure Hunt in a result
Examiner and estimate the AI terrain
- Identify the differences between KPIs, reported criteria, and root causes of the differences
- Identify the differences between anticipated and factual workflow outturn
- Maintain an AI result for nonstop enhancement
- Examiner AI factors for vacuity
- Recommend changes to an AI result grounded on performance data
Who should take the AI- 102 Designing and enforcing an Azure AI result test?
The AI- 102 test instrument is an internationally- honored instrument which help to have confirmation for Azure AI-102 Questions Engineers who have capability to negotiate the following specialized tasks dissect result conditions; design results; integrate AI models into results; and emplace and manage results.
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