Microsoft AI-300 exam voucher lets you purchase official exam credit for “Operationalizing Machine Learning and Generative AI Solutions.” You redeem it in the vendor or Pearson VUE scheduling portal, then choose your exam date and delivery option.
What this exam voucher includes
- An official Microsoft exam voucher code for AI-300 (Microsoft Certified: Azure AI Engineer Associate)
- Voucher redemption instructions for scheduling the exam through the vendor/Pearson VUE portal
- Country/region guidance for Lithuania availability
- Support via WhatsApp or Telegram to confirm your order reference and voucher delivery
- Help clarifying what to prepare before you book (based on the AI-300 skills outline)
- A single-use voucher code that you redeem to register for the exam date you select
Exam voucher price and what affects it
This voucher is sold at a discounted price of $38 USD versus a regular price of $165 USD in the pricing context provided.
Voucher pricing can vary due to:
- Vendor availability by country/region (here: Lithuania)
- Time-limited discounting by the reseller’s sourcing channels
- Peak vs. off-peak demand for scheduling exam seats
- Promotional adjustments by the vendor or distribution pathway
You are buying official exam credit, not a training course. The exam fee is redeemed when you schedule your AI-300 exam in the vendor or Pearson VUE portal.
| Item | What you get | Why it matters |
|---|---|---|
| Official Microsoft AI-300 exam voucher | Exam code for scheduling | Allows you to book the exam date |
| Discounted price | Lower cost than regular price (provided) | Keeps certification costs down |
| Delivery via chat | Code delivered over WhatsApp/Telegram after payment | Fast purchase experience |
Validity, expiry and rescheduling
Voucher codes have vendor-controlled validity and expiry. The exact expiry window can vary by issuance batch and the vendor’s current terms, so you should redeem as soon as you receive the code.
Rescheduling is handled by the scheduling portal rules after you book. Rescheduling windows, cutoffs, and any re-schedule fees (if applicable) are controlled by the vendor/Pearson VUE policy for your booking.
If you do not book before expiry, the voucher may become invalid. If you already booked an exam date, rescheduling is subject to the rules in your confirmation and the scheduling system.
How delivery and redemption work
- After payment is confirmed, we deliver the voucher via WhatsApp or Telegram to the contact details you provide.
- You receive the official AI-300 voucher code.
- You redeem the code in the Microsoft or Pearson VUE scheduling portal.
- After redemption, you can select your exam date and preferred delivery option where available.
- You complete the exam on the scheduled date.
You can then prepare for the assessed skills using the vendor exam skills outline and your own training plan.
Exam format at a glance
The Microsoft AI-300 exam validates your ability to operationalize machine learning and generative AI solutions, including deployment, monitoring, governance, and responsible AI considerations.
- Exam format: check the vendor page for the latest delivery details (remote or test center options can vary)
- Question types: typically scenario-based questions, practical concepts, and knowledge checks (exact breakdown can vary)
- Duration: check the vendor page for the latest allowed time
- Passing score: check the vendor page
- Languages: check the vendor page (English availability is common, but confirm)
AI-300 quick reference
| Exam code | Questions | Duration | Passing score | Languages | Delivery options |
|---|---|---|---|---|---|
| AI-300 | check the vendor page | check the vendor page | check the vendor page | check the vendor page | check the vendor page |
Domains (weighted)
Domain weighting can change over time; confirm the latest weights on the vendor exam page. Common AI-300 focus areas include:
- Operationalizing machine learning solutions (weight: check the vendor page)
- Operationalizing generative AI solutions (weight: check the vendor page)
- Responsible AI and governance in AI applications (weight: check the vendor page)
- Monitoring, performance, and lifecycle management (weight: check the vendor page)
Prerequisites
There are no universal “one-size-fits-all” prerequisites across all candidates, but you should expect working familiarity with:
- Data science concepts and ML workflow fundamentals
- GenAI concepts and prompt/response evaluation basics
- Cloud deployment and operational practices
- Security/governance and responsible AI practices
Recertification cycle
Microsoft certification policies can change. Use the vendor’s certification/retirement policy for the most current recertification guidance for the Azure AI Engineer Associate track.
Realistic study time
For experienced builders who already deploy ML/GenAI solutions:
- 4–8 weeks is a common target for focused preparation. For less experienced candidates:
- 8–12+ weeks may be more realistic.
Career value and job roles
An AI-300 credential supports roles such as:
- Azure AI / Machine Learning Engineer (mid-level to senior) who operationalizes models and GenAI into production services
- Applied AI Engineer who builds production inference, monitoring, and evaluation pipelines
- AI Solutions Architect who designs end-to-end AI systems with governance, observability, and reliability
Skills you strengthen with AI-300 typically map to real production work: model deployment patterns, evaluation and monitoring, and responsible AI requirements.
Related targets you may also consider (depending on your roadmap):
- ML engineering and MLOps-style learning paths on Azure
- GenAI solution engineering topics (prompting, evaluation, and safety controls)
- Governance, security, and operational excellence for AI workloads
Prepare before you book
Before you redeem and schedule, verify the latest exam details on the official AI-300 page. Then start practice with aligned content.
Use our free practice hub: free mock exams. Taking a passing mock exam helps unlock extra voucher discounts for eligible learners.
A practical approach:
- Review each domain and write a short checklist of what you must be able to do
- Focus on operational topics (deployment, monitoring, lifecycle, and governance)
- Do timed practice and review incorrect answers
- Confirm your scheduling preferences (online vs test center if available)
Common mistakes to avoid
- Waiting until near expiry: redeem and schedule quickly to avoid invalidation.
- Studying only theory: AI-300 emphasizes operationalization concepts, not just model training.
- Ignoring responsible AI: governance, safety, and mitigation strategies are core to production AI.
- Skipping monitoring/evaluation: production success depends on measurable quality and reliability.
- Booking without a plan: set a target date and practice consistently so you can pass on the first attempt.