AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK
Build practical skills in developing generative AI and agent solutions with Azure OpenAI and Semantic Kernel through prompts, plugins, functions, memory, orchestration, retrieval, security, evaluation, observability, and deployment through four structured lessons with demonstrations, hands-on activities, troubleshooting, validation checklists, and an applied improvement plan.
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-by SkillDynamo
- 0 Students
- Last updated
Course Description
This SkillDynamo course develops practical, role-relevant capability in developing generative AI and agent solutions with Azure OpenAI and Semantic Kernel through prompts, plugins, functions, memory, orchestration, retrieval, security, evaluation, observability, and deployment. The learning path progresses from essential concepts and planning to implementation, validation, troubleshooting, and operational use.
Learners work through realistic scenarios, guided demonstrations, applied exercises, and evidence-based reviews. Every topic is connected to workplace responsibilities, common risks, quality controls, and measurable outcomes.
The course helps professionals make informed decisions, identify failure points, communicate results, and create an actionable improvement plan for future projects.
What You Will Learn
- Explain and apply azure openai, semantic kernel and solution architecture within a practical AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK scenario
- Validate azure openai, semantic kernel and solution architecture decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes
- Explain and apply prompts, plugins, functions and agent orchestration within a practical AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK scenario
- Validate prompts, plugins, functions and agent orchestration decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes
- Explain and apply memory, retrieval, grounding and enterprise integration within a practical AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK scenario
- Validate memory, retrieval, grounding and enterprise integration decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes
- Explain and apply security, evaluation, monitoring and deployment within a practical AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK scenario
- Validate security, evaluation, monitoring and deployment decisions using requirements, configuration evidence, quality checks, risks, and measurable outcomes
SkillDynamo Training Approach
SkillDynamo delivers this course through demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, and communication so the skills transfer confidently into real workplace situations.
Curriculum
- 1 Section
- 4 Lessons
- 8 Hours
- Course Curriculum4 Items
- 1.1AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK — Lesson 1: Azure OpenAI, Semantic Kernel and Solution Architecture2 hours
- 1.2AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK — Lesson 2: Prompts, Plugins, Functions and Agent Orchestration2 hours
- 1.3AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK — Lesson 3: Memory, Retrieval, Grounding and Enterprise Integration2 hours
- 1.4AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK — Lesson 4: Security, Evaluation, Monitoring and Deployment2 hours
SkillDynamo Expert Team
Instructor-led delivery focused on AZ-2005: Develop Generative AI Solutions Using Azure OpenAI and the Semantic Kernel SDK with practical demonstrations, implementation guidance, validation, and troubleshooting.
- Structured explanations and role-focused demonstrations
- Applied exercises, scenario analysis, and solution reviews
- Course-specific validation, support, and improvement guidance
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