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AI-300Microsoft ML Operations Engineer
AI-300 · 30 days left ›
PREDICTED786 ±37✓ Exam Ready · 87% confidence
AI-300 Practice Questions & Exam Prep — Microsoft ML Operations Engineer
Get exam-ready for AI-300 (Microsoft ML Operations Engineer) on iPhone or iPad. Azure Mastery uses on-device AI to predict your readiness score across all five AI-300 domains, build a personalised study plan from your weak spots, and surface topics you're forgetting. Core study stays on-device and works offline; optional sync uses your private iCloud account.
The exam
What is the AI-300 exam?
AI-300 is the Microsoft Certified: ML Operations Engineer Associate exam — the credential hiring managers expect when posting "MLOps Engineer", "GenAIOps Engineer", "AI Platform Operations", or "ML Reliability Engineer" roles. AI-300 covers the operational lifecycle of ML and generative-AI systems: MLOps infrastructure, model lifecycle, GenAIOps observability, and performance tuning at scale.
AI-300 is hands-on and lifecycle-focused. It validates that you can:
Design and implement an MLOps infrastructure — CI/CD for ML, environment management, registries, governance
Implement machine learning model lifecycle and operations — training pipelines, deployment, monitoring, retraining
Design and implement a GenAIOps infrastructure — prompt versioning, evaluation pipelines, deployment patterns for LLM apps
Implement generative AI quality assurance and observability — groundedness checks, drift, hallucination detection, telemetry
Optimise generative AI systems and model performance — caching, batching, model selection, fine-tuning
Expect scenario questions that span CI/CD configs, monitoring strategies, and trade-off reasoning.
Microsoft updated the AI-300 skills outline in March 2026. Every question in Azure Mastery's AI-300 bank is mapped to the current outline — no leftover questions on retired services. Read the official outline at learn.microsoft.com.
Questions40–60 multiple choice
Duration100 minutes (120 min seat)
Pass score700 / 1000
CostUSD $165 (≈ £128 UK)
ValidityRenew annually (Associate)
FormatOnline or test centre
Skills measured · 26 July 2026
AI-300 exam objectives
Five domains, skills measured as of 26 July 2026 on the official skills outline. Every domain below lists Microsoft's own skill groups verbatim, so you always know which domain you're being tested on and where your weak spots cluster.
AI & agent skill mapDataModelsAgentsResponsible AI
Design and implement an MLOps infrastructurePublished weight 15–20%
70 exam-scoped practice questions in the app
Explore MLOps Infra topics
Create and manage resources in a Machine Learning workspace
Create and manage assets in a Machine Learning workspace
Implement IaC for Machine Learning
Tests whether you reach for a registry, a shared datastore, or IaC to solve a described infrastructure need.
A network restriction configured too tightly can block the exact workflow it was meant to secure.
Learn which Bicep or CLI script handles reproducible workspace deployment versus a manual portal setup.
Implement machine learning model lifecycle and operationsPublished weight 25–30%
102 exam-scoped practice questions in the app
Explore ML Lifecycle topics
Orchestrate model training
Implement model registration and versioning
Deploy machine learning models for production environments
Monitor and maintain machine learning models in production
Matching a training, registration, deployment, or monitoring step to what the scenario actually needs done is the core skill.
Candidates treat "registered" and "deployed" as the same milestone, missing that deployment is a separate step.
Watch for a data-drift signal in monitoring questions; that's the cue for a retraining or alert trigger.
Design and implement a GenAIOps infrastructurePublished weight 20–25%
84 exam-scoped practice questions in the app
Explore GenAIOps topics
Implement Foundry environments and platform configuration
Deploy and manage foundation models for production workloads
Implement prompt versioning and management with source control
Tests configuring Foundry environments, model deployment, and prompt versioning for a stated production need.
Provisioned throughput often gets reached for on a spiky, low-volume workload where a Standard pay-per-token deployment fits better.
Match identity, network, and deployment choices to the described security and traffic pattern, not habit.
Implement generative AI quality assurance and observabilityPublished weight 10–15%
52 exam-scoped practice questions in the app
Explore QA & Observe topics
Configure evaluation and validation for generative AI applications and agents
Implement observability for generative AI applications and agents
Building an evaluation harness before launch is what separates a strong answer, well before problems reach production.
Classic drift monitoring often gets applied to a GenAI system instead of tracking groundedness or relevance decay.
Learn which metric, groundedness, relevance, coherence, or fluency, actually measures the described failure.
Optimize generative AI systems and model performancePublished weight 10–15%
54 exam-scoped practice questions in the app
Explore Optimize topics
Optimize retrieval-augmented generation (RAG) performance and accuracy
Implement advanced fine-tuning and model customization
Knowing when tuning retrieval beats fine-tuning the model, or neither beats a better base model, is the core test.
Candidates tune an embedding model when a compound query was never fully expressed to the retriever.
Prove any RAG or fine-tuning improvement with a relevance metric or A/B test rather than instinct.
Common traps
Where AI-300 candidates slip
Five recurring misconceptions that trip up otherwise well-prepared AI-300 candidates, grounded in the current skills outline.
Registering an MLflow model captures the artifact; it doesn't mean the model is production-ready.
A model sitting in the registry with no traffic hasn't been deployed yet, whatever the registration status suggests.
Check for a real-time or batch endpoint and a rollout strategy before assuming registration finished the job.
Data drift and prompt drift are caught by different monitoring mechanisms.
A GenAIOps workload calls for evaluation-metric decay, not classic drift monitoring, as the signal to watch.
Match feature-distribution drift to traditional models and groundedness or relevance decay to generative systems.
A better embedding model doesn't fix a query the retriever never saw.
A missed compound question is a query-construction problem first, not a scoring problem.
Check whether every condition in the question was actually expressed before tuning the embedding model.
Provisioned throughput solves capacity and latency, not cost, for a spiky workload.
A low-volume, spiky workload with a cost constraint usually calls for a Standard pay-per-token deployment, not PTUs.
Match billed-regardless-of-usage PTUs to steady high volume, and pay-per-token to unpredictable traffic.
A network restriction can silently block the pipeline it was meant to protect.
A missing rule or managed identity is what typically breaks right after a workspace or project gets locked down.
Grant the specific pipeline, registry share, or monitoring integration through the boundary you just added.
Designed for AI-300
How Azure Mastery helps you pass AI-300
Exam-specific practice
362 AI-300 practice questions, each written against the current 26 July 2026 skills outline.
Every question is tagged to one of AI-300's five official domains.
Practise deployment ordering and infrastructure-as-code snippets, the exam's actual scenario style.
Predicted score
Exam IQ forecasts your AI-300 score on-device after roughly 30 questions, with a confidence range attached.
It names the specific training-orchestration or monitoring topic dragging your score down.
See which of the five domains is weighing your score down before you book.
Adaptive study plan
Your plan leans hardest on model lifecycle and operations, the domain that outweighs every other section.
Miss a foundation-model deployment or RAG-tuning question and the next session surfaces that domain again.
Three consecutive accurate sessions on a topic and the engine deprioritises it.
Knowledge decay
Five domains spanning traditional MLOps and GenAIOps is a lot to retain without regular revisiting.
The Today screen surfaces a padlock icon the moment a topic starts fading.
Focus Weak Spots sessions prioritise whatever has decayed the most.
Exam rehearsal
The simulator locks in a full 100-minute run with no flag-and-review, mirroring the real exam.
It draws only original Azure Mastery content, weighted to the published domain split.
The live AI-300 interface and question mix are Microsoft's to control, not ours.
Answer Coach
Untangles AI-300's near-identical calls: registration vs deployment, provisioned throughput vs a Standard pay-per-token deployment.
Each explanation points to the exact line in the stem that settled the answer.
On capable devices a note can be reworded locally, only once it's checked against the source.
Aura guidance
Aura adjusts what it suggests as your first week of AI-300 sessions goes on.
A short summary waits at the end of each session: what changed, what's next.
Private by design
Your AI-300 answer history, readiness gauge, and Answer Coach notes stay private by default.
Nothing is shipped off-device that doesn't need to be, and no account is required.
Optional sync uses your private iCloud account.
6-week study plan
Suggested AI-300 study plan
Work through ML operations, then generative AI operations and optimisation. Use this six-week route to connect infrastructure choices with model quality and reliability.
MLOps infrastructure
Plan CI/CD and reproducible Conda or Docker environments.
Review feature stores and model registries.
Track governance, lineage and reproducibility.
Model lifecycle
Build training pipelines and explore AutoML.
Deploy online or batch endpoints and compare traffic-splitting choices.
Monitor drift and define retraining triggers.
GenAIOps infrastructure
Version prompts and build evaluation pipelines.
Compare deployment patterns and feature-flag rollouts.
Track token usage and cost.
Quality and observability
Evaluate groundedness and hallucinations.
Review content-safety telemetry and prompt or RAG drift.
Use distributed tracing to investigate failures.
Performance and cost
Compare semantic caches, embedding caches and batching.
Choose models and weigh fine-tuning, few-shot prompting and RAG.
Plan token budgets and capacity.
Review and rehearse
Use Focus Weak Spots to revisit weaker topics.
Complete two timed, 100-minute Exam Simulator sessions.
Review mistakes and revisit the underlying trade-offs.
Inside the app
Nine interactive practice formats, on iPhone
Azure Mastery has nine interactive formats for exam practice. The examples below show original AI-300 practice questions in Azure Mastery on iPhone and iPad. The exam simulator uses timed sessions with the published domain weights. Microsoft's live interface and question mix may differ.
Which infrastructure-as-code language is natively supported by Azure and compiles to ARM templates?
Terraform
Pulumi
Bicep
Ansible
Multiple choice
An original AI-300 practice question with one correct answer. The app explains every option after you answer.
Exam-specific sample
Which TWO types of drift can Azure Machine Learning model monitoring detect? (Choose two.)
Data drift
Code drift
Prediction drift
Network drift
Multi-select
An original AI-300 multi-select question. Select all the correct options to earn the mark.
All-or-nothing
An organization will use precreated associated resources and automatic managed-network provisioning. Arrange the workspace setup steps in the correct…
⋮⋮1Create the resource group and precreate the associated Storage account…
⋮⋮2Provision the Azure Machine Learning workspace with managed VNet…
⋮⋮3Configure outbound rules for private endpoints and service tags
⋮⋮4Create compute resources to trigger managed VNet provisioning
Drag-and-drop
An original AI-300 ordering question you can answer by touch on iPhone and iPad.
Interactive item
In Azure Machine Learning studio, select the section and option to create a new compute cluster for training.
Hotspot
An original AI-300 question with a visual prompt, designed for touch on iPhone and iPad.
Tap target
Contoso Claims Assistant Contoso is preparing a claims assistant that uses retrieval-augmented generation over approved policy documents. The release gate requires grounded answers with…
1Which dataset design best supports Contoso's release decision?
2An answer is fluent and logically organized but contradicts the retrieved policy…
3Policy users search with exact clause identifiers and with natural-language…
4Several policy exceptions span two adjacent paragraphs and are missed after…
Case studies
An original AI-300 case study with several questions about the same requirements and environment.
Multi-question
✕Your answer: Terraform
✨ Answer Coach:Terraform is a third-party HashiCorp tool that provisions Azure resources through the AzureRM provider against its own state model; it does not emit or compile to ARM templates. Because it is not Azure's…
— grounded in authored certification guidance
Answer Coach
Answer Coach explains why the answer is correct and, where available, why each option is right or wrong. It helps you understand a mistake and remember the distinction. Read explanations after each question or at the end of a practice test. On supported devices, optional AI can reword a note on your device after checking it against the written guidance.
App exclusive
Frequently asked
AI-300 FAQs
Did AI-300 replace DP-100?
Yes. DP-100 (Azure Data Scientist Associate) retired on 1 June 2026, and AI-300 is its named successor. The scope has grown to match: DP-100 covered training and deploying traditional ML models, while AI-300 adds GenAIOps — deploying, evaluating, and monitoring generative AI built with Microsoft Foundry.
Do I need AI-200 before AI-300?
Not a formal prerequisite, but it helps. AI-200 (Azure AI Cloud Developer Associate) builds the cloud platform AI workloads run on — compute, vector databases, integration, security. AI-300 (ML Operations Engineer Associate) operates ML and generative-AI systems on that platform, so most candidates take AI-200 first.
Are there prerequisites for AI-300?
Microsoft sets no mandatory prerequisite exam for AI-300. Its published audience profile expects MLOps and GenAIOps subject-matter expertise, a data-science background with Python, and entry-level DevOps familiarity such as GitHub Actions. Without hands-on Azure Machine Learning or Foundry experience, preparation typically takes longer.
What format is the AI-300 exam?
Like most Associate exams, AI-300 runs a general 40–60 question format that Microsoft varies by sitting. Azure Mastery's bank leans on single-answer and scenario-based items for most of its weight, then rounds out with drag-to-match, dropdown-select, and hot-area questions plus linked case studies.
Is AI-300 a permanent credential, or does it need renewing?
It needs renewing. Microsoft Associate certifications including AI-300 expire annually. Renewal is a free, short online assessment on Microsoft Learn within the six months before expiry. Fundamentals certifications such as AZ-900 don't expire.
What does the AI-300 exam voucher cost?
The AI-300 voucher is USD $165 in the United States, or around £128 in the UK. Microsoft sometimes runs free-voucher promotions around events like Build or Ignite, so check your Learn profile before booking. Renewal itself is free, but it happens every year.
How soon can I resit AI-300 after a fail?
The wait depends on your attempt number, and every attempt needs its own voucher.
First retake: after 24 hours.
Second and third retakes: a 14-day wait each.
Cap: five attempts per rolling 12 months.
Can I revise for AI-300 without a connection?
Yes — the whole loop stays on your device: answering questions, scoring them, and building the readiness prediction. There's no account to sign into, and your study history stays local unless you switch on private iCloud sync.
Is Azure Mastery free to try for AI-300?
Yes — the app is free to download, with a free allowance of AI-300 questions so you can try every feature. The full bank of 362 AI-300 practice questions unlocks with a one-time exam-pack purchase, or unlock every exam with a subscription or lifetime upgrade.
Free study guides
Free guides that pair with AI-300
Where AI-300 fits
Certification paths that include AI-300
AI-300 is the Microsoft Machine Learning Operations Engineer Associate certification. It replaced the retired DP-100 route and pairs with AI-200 for engineers who build the surrounding Azure AI platform as well as operate models and generative AI systems.
AI-300 sits at the ML and generative-AI operations Associate tier. AI-901 supplies the current fundamentals foundation; AI-200 is the complementary Azure AI cloud-development credential.
Ready to pass AI-300?
Download Azure Mastery free. 362 AI-300 practice questions across all five domains, AI score prediction, full-length exam simulator, adaptive study plan. iPhone & iPad.
