Microsoft AI-300 exam voucher credits for the Operationalizing Machine Learning and Generative AI Solutions (MLOps Engineer) exam. Buy the voucher through ITExamDeals and redeem the voucher code in the vendor scheduling portal to choose your exam date.
What this exam voucher includes
- 1 official exam voucher code for Microsoft AI-300 (Operationalizing Machine Learning and Generative AI Solutions (MLOps Engineer))
- Voucher redemption instructions to book your exam in the official scheduling portal (Microsoft/Pearson VUE)
- Support via WhatsApp or Telegram for ordering confirmation and redemption steps
- Access to the correct exam selection guidance for AI-300 (avoid booking the wrong exam)
- Eligibility for the Dominican Republic ordering workflow handled by ITExamDeals
Exam voucher price and what affects it
This voucher is discounted by ITExamDeals for your region.
| Item | Regular price | Your selling price |
|---|---|---|
| Microsoft AI-300 exam voucher (USD) | $165 | $39 |
Voucher pricing can be influenced by the vendor’s region policy, scheduling partner rules (Microsoft/Pearson VUE), and the reseller’s discounted sourcing.
Validity, expiry and rescheduling
Voucher credits come with vendor-controlled validity. The expiry date (and any deadline to redeem the code) is determined by Microsoft/Pearson VUE rules associated with the issued voucher.
Rescheduling is handled through the official scheduling portal. If you need to change your exam date, you must do so within the rescheduling window set by the vendor. If you miss the booking or do not attend (no-show), standard vendor consequences may apply.
How delivery and redemption work
- After your payment is confirmed, ITExamDeals delivers the voucher code to you by WhatsApp or Telegram.
- You redeem the voucher code in the official Microsoft/Pearson VUE scheduling portal.
- After redemption, you can select an available exam slot and complete the exam under the AI-300 listing.
- Keep your confirmation messages (order reference + voucher code) in case you need help aligning the booking to the correct exam.
Exam format at a glance
| 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
The Microsoft AI-300 (MLOps Engineer) credential is aimed at professionals who deploy, operationalize, and improve machine learning and generative AI solutions in production.
Common roles include:
- MLOps Engineer (entry to mid-level): build CI/CD and deployment pipelines for ML/GenAI
- AI Engineer / ML Engineer (mid-level): productionize training and inference with monitoring and governance
- Data/ML Platform Engineer (senior): manage end-to-end operational workflows, reliability, and lifecycle management
This exam reinforces skills around:
- Operationalizing ML workflows
- GenAI solution deployment patterns
- Pipeline automation, evaluation, and model lifecycle management
Prepare before you book
Use a structured plan before you redeem and schedule.
Start with these steps:
- Review the AI-300 skills and map them to hands-on practice in Azure AI and MLOps workflows.
- Create a checklist for the service areas you must demonstrate (deployment, monitoring, evaluation, and lifecycle operations).
- Practice exam questions under time pressure.
For preparation, use our hub: free mock exams. Completing a mock exam helps you identify gaps early, and passing a mock exam unlocks extra voucher discounts.
Common mistakes to avoid
- Booking the wrong exam: confirm the exam code AI-300 before scheduling.
- Waiting until after redemption to study: the voucher schedule can require you to move quickly once you book.
- Only learning theory: focus on operational workflows (deployment, monitoring, and lifecycle management).
- Ignoring the language/location rules: scheduling options can vary by country and testing center/online availability.
- Assuming unlimited reschedules: rescheduling rules are vendor-controlled—check timelines before you commit.