Microsoft AI-300 is the exam for Operationalizing Machine Learning and Generative AI Solutions (MLOps Engineer). This official exam voucher from Microsoft is a vendor exam credit you redeem in the Microsoft/Pearson VUE scheduling portal to schedule your AI-300 test.
What this exam voucher includes
- Microsoft AI-300 official exam voucher code (vendor exam credit)
- Redemption instructions for scheduling in the Microsoft/Pearson VUE portal
- Country eligibility support for Niger
- Order confirmation message after payment
- Guidance on booking steps (no exam guarantee)
Exam voucher price and what affects it
This AI-300 voucher is discounted by ITExamDeals based on reseller program pricing and fulfillment availability. Your provided price context is:
| Pricing item | Amount |
|---|---|
| Regular price (reference) | $165 |
| Selling price (today) | $39 |
Voucher pricing can vary due to vendor pricing tiers, country availability, and promotional inventory. The voucher you receive is still genuine Microsoft exam credit—only the purchase price differs.
Validity, expiry and rescheduling
Voucher validity and expiry are controlled by Microsoft (and any partner rules shown in the booking portal). After you receive the voucher code, you must redeem and book within the vendor’s allowed timeframe. Rescheduling is also governed by Microsoft/Pearson VUE policies; you can generally choose a new date if you do it within the vendor-defined reschedule window and meets testing requirements.
How delivery and redemption work
- You place the order for the Niger-eligible voucher.
- After payment confirmation, we deliver the voucher code and booking instructions via WhatsApp or Telegram.
- You redeem the voucher code in the Microsoft or Pearson VUE exam scheduling portal.
- You choose your test date/time and complete any required account steps.
Exam format at a glance
Review the latest details in the official exam page and the vendor scheduling flow.
| Exam code | Exam name | Questions | Duration | Passing score | Languages | Delivery options |
|---|---|---|---|---|---|---|
| AI-300 | Operationalizing Machine Learning and Generative AI Solutions (MLOps Engineer) | check the vendor page | check the vendor page | check the vendor page | check the vendor page | English (typical) — check the vendor page |
In general, Microsoft associate-level exams include a mix of scenario-based tasks and concept/implementation questions that test your ability to operationalize machine learning and generative AI using MLOps practices.
Domains (weighted)
Domain percentages can change, so confirm in the official exam guide. Commonly assessed areas for AI-300 include the following themes:
- MLOps implementation, orchestration, and pipelines — check the vendor page
- Operationalizing ML systems and managing lifecycle — check the vendor page
- Deploying and managing models (including generative AI considerations) — check the vendor page
- Monitoring, evaluating, and improving production systems — check the vendor page
Career value and job roles
This AI-300 credential supports careers such as:
- MLOps Engineer / ML Operations Engineer (mid-level)
- Cloud AI Engineer implementing production ML/LLM workflows
- Data Scientist transitioning into deployment, monitoring, and governance
You should expect to work with CI/CD for ML, model/version management, deployment patterns, and operational monitoring for ML and generative AI solutions.
Prepare before you book
Use a practice-and-review approach before you redeem and schedule.
- Start with the official exam skills outline.
- Build hands-on familiarity with Azure MLOps concepts and deployment patterns.
- Take timed practice to measure readiness.
For extra prep value, use our hub: free mock exams. Passing a mock exam unlocks extra voucher discounts.
Common mistakes to avoid
- Waiting until the last moment: confirm voucher redemption steps immediately after delivery.
- Studying only theory: focus on operational tasks like pipeline workflows, deployment management, monitoring, and evaluation.
- Ignoring generative AI operations: ensure you understand how MLOps applies to LLM-style workloads (evaluation, safety/quality considerations where applicable, and production readiness).
- Not checking domain weights: use the official AI-300 guide to prioritize what’s most heavily assessed.
- Booking without a plan: schedule only after you’ve completed a practice run and are comfortable with the exam-style questions.