Lesson proof
Concept, demo, checklist, lab, and assignment evidence.
AI / Generative AI / AI Engineer
AI Engineer
Prepare for AI engineering work across prompt design, retrieval augmented generation, vector search, evaluation, safety, governance, and AI application deployment.
AI app designRAG readinessAI governance
Start domain mock testPlatform-wide module outputs
Every module now feeds portfolio proof and CV readiness.
Portfolio pack
Requirement, artifact, validation, risk note, and interview story.
CV signal
Role-specific skill statement linked to a score or artifact.
Review queue
Submitted evidence can support dashboard, readiness, and career exports.
Open materials
Microsoft Azure AI Apps and Agents Developer Associate (AI-103)Lesson + portfolio pack + CV evidenceAI engineering foundationsLesson + portfolio pack + CV evidencePrompt designLesson + portfolio pack + CV evidenceEmbeddings and vector searchLesson + portfolio pack + CV evidenceRetrieval augmented generationLesson + portfolio pack + CV evidenceAI evaluation and safetyLesson + portfolio pack + CV evidenceAI deployment and governanceLesson + portfolio pack + CV evidence
Certification objective coverage
Every provider-aligned module is connected to a lesson, labs, mock questions, and implementation proof.
This is the track-level audit view for blueprint alignment. Exact exam wording should still be checked against the current official provider guide before public exam-code claims are made.
ai-engineer.microsoft-azure-ai-apps-and-agents-developer-associate-ai-103.01 / 14% weight
Apply Microsoft Azure AI Apps and Agents Developer Associate (AI-103) decisions to AI Engineer scenarios
Implementation proof
- Define Microsoft Azure AI Apps and Agents Developer Associate (AI-103) in plain language and explain the provider service family it belongs to.
- Show how Microsoft Azure AI Apps and Agents Developer Associate (AI-103) is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.ai-engineering-foundations.02 / 14% weight
Apply AI engineering foundations decisions to AI Engineer scenarios
Implementation proof
- Define AI engineering foundations in plain language and explain the provider service family it belongs to.
- Show how AI engineering foundations is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.prompt-design.03 / 14% weight
Apply Prompt design decisions to AI Engineer scenarios
Implementation proof
- Define Prompt design in plain language and explain the provider service family it belongs to.
- Show how Prompt design is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.embeddings-and-vector-search.04 / 14% weight
Apply Embeddings and vector search decisions to AI Engineer scenarios
Implementation proof
- Define Embeddings and vector search in plain language and explain the provider service family it belongs to.
- Show how Embeddings and vector search is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.retrieval-augmented-generation.05 / 14% weight
Apply Retrieval augmented generation decisions to AI Engineer scenarios
Implementation proof
- Define Retrieval augmented generation in plain language and explain the provider service family it belongs to.
- Show how Retrieval augmented generation is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.ai-evaluation-and-safety.06 / 14% weight
Apply AI evaluation and safety decisions to AI Engineer scenarios
Implementation proof
- Define AI evaluation and safety in plain language and explain the provider service family it belongs to.
- Show how AI evaluation and safety is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
ai-engineer.ai-deployment-and-governance.07 / 16% weight
Apply AI deployment and governance decisions to AI Engineer scenarios
Implementation proof
- Define AI deployment and governance in plain language and explain the provider service family it belongs to.
- Show how AI deployment and governance is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
Test readiness
Mock test by domain
Practice every domain in this track with exam-style questions, answer keys, and explanations.
Open mock testMost in-demand certification materials
High-value certificates connected to this track.
Microsoft
Microsoft Azure AI Apps and Agents Developer Associate (AI-103)
Needs reviewLast verified: Not verifiedNext review: Provider source review required
AI engineers building, managing, and deploying Azure AI apps and agents with Microsoft Foundry, Python, generative AI, vision, language, extraction, and responsible AI controls.
Microsoft Azure AI Apps and Agents Developer Associate (AI-103) is mapped to platform lessons and labs, but still needs a dated official-source review.
- AI service selection checklist
- Prompt and RAG design workbook
- Responsible AI and evaluation rubric
AWS
AWS Certified AI Practitioner
CurrentLast verified: 2026-06-24Next review: 2026-09-22
Learners building AI literacy and generative AI foundations for cloud roles.
AIF-C01 was verified against the official AWS certification page on 2026-06-24. Keep this source check on the 90-day review cadence.
AWS official-source stamp
Foundational / AIF-C01
Official domain weighting
Fundamentals of AI and ML20%
Fundamentals of Generative AI24%
Applications of Foundation Models28%
Responsible AI14%
Security, Compliance, and Governance for AI Solutions14%
Lab focus
- AI service selection
- Responsible AI
- Privacy risk
- Evaluation and business value
Readiness gates
- AI concepts complete
- Responsible AI checklist complete
- Use-case decision note approved
- Weak AI domains remediated
- AI vocabulary and use-case map
- Generative AI risk checklist
- Model selection and grounding notes
Certification provider connections
Connect this learning path to the official exam provider.
Microsoft Learn Credentials
Microsoft Azure AI Apps and Agents Developer Associate (AI-103)
Connect the learner's Microsoft Learn profile before booking so exam discounts, transcripts, renewals, and badges stay together.
Booking partner: Pearson VUE
- Create or confirm the Microsoft Learn profile.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
AWS Certification
AWS Certified AI Practitioner
Use the AWS Certification account to review exam guides, book exams, manage score reports, and share verified badges.
Booking partner: Pearson VUE or PSI, depending on exam and region
- Create or confirm the AWS Certification account.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
01 Match
Map each Daskerel track to the official provider, exam code, registration page, and verification route.
02 Prepare
Use provider objectives with Daskerel lessons, mock exams, labs, and evidence packs before booking.
03 Book
Send learners to the official scheduling partner while keeping target dates and next actions in the dashboard.
04 Verify
Capture certificate URL, badge, expiry, renewal plan, and portfolio proof after the learner passes.
Study plan
Learn the difference between model capability, application workflow, context, retrieval, tools, and evaluation.
Practise designing prompts, grounding answers in trusted content, and testing hallucination risk.
Add safety, privacy, logging, human review, and governance controls before production use.
Hands-on labs
Design a RAG workflow for a certification knowledge base.
Write evaluation checks for answer accuracy, refusal quality, and citation grounding.
Create an AI governance checklist for learner support and content recommendations.
Track learning assets
Templates and revision tools for this path.
Exam blueprint checklistAI Engineer
Weekly study plannerAI Engineer
Command and service cheat sheetAI Engineer
Architecture pattern cardsAI Engineer
Flashcard revision setAI Engineer
Mock exam review sheetAI Engineer
Lab evidence templateAI Engineer
Interview story builderAI Engineer
Portfolio project rubricAI Engineer
Final readiness checklistAI Engineer
Practice questions
What problem does retrieval augmented generation help solve?
It grounds model responses in selected knowledge sources so answers can be more specific, current, and auditable.
Why does an AI application need evaluation?
Evaluation checks whether outputs are accurate, safe, relevant, consistent, and aligned with the intended user task.