Microsoft AI-300 is the certification exam for Operationalizing Machine Learning and Generative AI Solutions (MLOps Engineer). This discounted exam voucher lets you redeem a genuine vendor exam credit and schedule your assessment when you’re ready.
What this exam voucher includes
- A Microsoft AI-300 exam voucher code (official exam credit)
- Voucher redemption instructions for booking in the vendor scheduling portal
- Your order reference details tied to the voucher delivery
- Support via WhatsApp or Telegram for redemption and next-step guidance
- Confirmation of voucher delivery after payment is received
Exam voucher price and what affects it
Your discounted price for this Microsoft AI-300 voucher is $39 USD (regular price $165 USD). Exam voucher pricing can vary due to:
- Vendor-controlled promos and reseller discount availability
- Regional availability (country/region scheduling rules)
- Voucher type and redemption window (vendor terms)
- Timing of the purchase relative to vendor promo cycles
| Item | What you get | Price (USD) |
|---|---|---|
| Exam voucher (Microsoft AI-300) | Official exam credit redeemed to schedule AI-300 | $39 |
| Regular price | Typical list pricing (vendor) | $165 |
Validity, expiry and rescheduling
Voucher validity is controlled by the exam vendor terms. Your voucher code will have an expiry window set by Microsoft (often expressed as a vendor-defined redemption/validity period). If the voucher expires before you schedule, it cannot be used.
Rescheduling is allowed only within the scheduling rules of the exam provider (Microsoft and/or Pearson VUE). The rescheduling window, deadlines, and any fees are set by the vendor/provider. Plan to book promptly after you receive the voucher code.
How delivery and redemption work
After payment is confirmed, we deliver the voucher by sending the official exam code and booking guidance to you over WhatsApp or Telegram. You then redeem the code in the Microsoft or Pearson VUE exam scheduling portal to choose your exam date and location.
Typical flow:
- Receive voucher code and instructions in chat
- Redeem the code in the vendor scheduling portal
- Select your test center or online option (where available for your region)
- Book your appointment
Exam format at a glance
Microsoft AI-300 focuses on applying MLOps practices to machine learning and generative AI solutions. The exam is delivered by Microsoft and/or Pearson VUE using computer-based testing.
- Question types: typically multiple choice and scenario-based questions (check the vendor page for exact breakdown)
- Duration: check the vendor page
- Passing score: check the vendor page
- Language: check the vendor page
- Retakes: allowed as separate attempts, subject to voucher availability and vendor rules
| 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 |
Career value and job roles
This Microsoft AI-300 voucher is a strong fit for MLOps Engineer roles and for engineers who operationalize AI systems from prototypes into production.
Common job targets include:
- MLOps Engineer (ML/GenAI deployment, CI/CD, monitoring)
- Machine Learning Engineer (productionization and model lifecycle)
- Cloud AI Engineer / Applied AI Engineer (end-to-end AI solution operations)
The skills validated map to real work such as deploying ML/GenAI services, managing model versions, implementing governance and monitoring, and ensuring reliable pipelines.
Prepare before you book
Before you redeem your voucher, confirm your exam readiness with practice.
Use our free practice hub: free mock exams. Completing a passing mock exam can unlock extra voucher discounts (as provided in the hub’s offer details). Use the mock results to identify weak domains and schedule confidently.
Common mistakes to avoid
- Waiting too long after delivery: voucher expiry and booking availability are time-sensitive
- Studying only model training: AI-300 emphasizes operationalization (deployment, monitoring, lifecycle)
- Ignoring governance and evaluation: generative AI operational work includes risk controls and quality practices
- Underestimating lab-like workflows: you’ll need practical understanding of MLOps concepts
- Booking without a plan: you should schedule based on your readiness, not just after you buy the voucher