AI-300 — Microsoft Certified: Machine Learning Operations Engineer Associate Study Plan

Last revised: June 18, 2026

A practical AI-300 study plan for Microsoft Certified: Machine Learning Operations Engineer Associate candidates, with 7-day, 14-day, 30-day, and 60/90-day preparation paths.

Study Plan Orientation

This study plan is for candidates preparing for Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) from Microsoft. It is designed for professionals who need a structured way to prepare for the real AI-300 exam while balancing work, project duties, and hands-on practice.

AI-300 preparation should focus on practical MLOps judgment in Microsoft Azure environments, including:

Azure Machine Learning workspaces, assets, jobs, environments, models, endpoints, and registries
Machine learning pipeline design and automation
CI/CD and Git-based MLOps workflows
Model deployment, promotion, rollback, and operational monitoring
Data, feature, model, and environment versioning
Security, identity, access control, governance, and responsible AI considerations
Troubleshooting failed jobs, broken deployments, drift, observability gaps, and pipeline reliability issues

Use the schedule that matches your available time. If you are unsure, start with a diagnostic practice session before choosing a path.

Which Plan Should You Use?

Time Until Exam	Use This Plan	Best For	Daily Study Target	Main Risk
7 days	Final review plan	Candidates who already studied and need consolidation	2 to 4 hours	Too much new material too late
14 days	Focused plan	Candidates with Azure ML or MLOps experience who need exam alignment	1.5 to 3 hours	Skipping hands-on weak areas
30 days	Balanced plan	Most working professionals	60 to 120 minutes	Reviewing concepts without timed practice
60/90 days	Full preparation path	Candidates newer to Azure ML, CI/CD, or production ML operations	45 to 90 minutes	Moving too slowly without checkpoints

Quick Decision Checklist

Choose the shorter plan only if most of these are true:

You can explain the difference between training jobs, pipeline jobs, registered assets, models, endpoints, and deployments.
You have used or reviewed Azure Machine Learning workflows.
You understand why MLOps uses source control, reproducible environments, versioned data, and automated promotion.
You can reason through deployment choices and monitoring scenarios.
You can answer scenario questions under time pressure.

If not, use the 30-day or 60/90-day plan.

Baseline Diagnostic Before You Start

Before following any schedule, complete a diagnostic session.

Step	Time	What To Do	Output
1	20 minutes	Review the AI-300 exam page and official Microsoft skill outline	List major skill areas
2	45 to 60 minutes	Take a mixed set of practice questions without notes	Baseline score and weak areas
3	30 minutes	Review every missed and guessed question	Error log
4	20 minutes	Rank weak areas by impact	Top 3 study priorities
5	15 minutes	Choose your plan	7, 14, 30, or 60/90 days

Do not use the diagnostic score as a prediction. Use it to decide where your study time should go.

Core Study Areas for AI-300

Organize your preparation around practical MLOps workstreams rather than memorizing isolated facts.

Study Area	What To Know	Hands-On or Scenario Practice
Azure Machine Learning workspace fundamentals	Workspaces, compute, jobs, experiments, assets, environments, models, endpoints	Trace how a model moves from training to deployment
ML pipelines	Pipeline components, reusable steps, inputs, outputs, dependencies, scheduling, reproducibility	Design a training and evaluation pipeline
MLOps with source control	Branching, pull requests, CI validation, release gates, artifact promotion	Map code changes to pipeline execution
CI/CD for ML	Build, test, package, register, deploy, validate, rollback	Identify what belongs in CI vs CD
Data and model versioning	Registered datasets/data assets, model versions, environment versions, lineage	Explain how to reproduce a previous model
Deployment operations	Online and batch deployment concepts, endpoint testing, traffic control, rollback	Choose deployment strategy for a scenario
Monitoring and observability	Job logs, endpoint health, model performance, data drift, alerts, failure diagnosis	Investigate why model quality or endpoint reliability changed
Security and governance	Identity, role-based access, managed identities, secrets, network controls, approval workflows	Select secure access patterns
Responsible AI and operational risk	Explainability, fairness, documentation, auditability, human review	Decide what checks belong before production
Troubleshooting	Failed runs, dependency issues, permissions, bad data, environment mismatch, deployment errors	Build a root-cause checklist

Daily Practice Rhythm

Use the same rhythm regardless of plan length. Shorten or expand the blocks depending on available time.

60-Minute Daily Session

Block	Time	Activity
Warm-up recall	5 minutes	Write what you remember from yesterday without notes
Focused learning	20 minutes	Study one AI-300 topic or Microsoft Learn section
Applied review	15 minutes	Work through a scenario, architecture choice, or hands-on task
Practice questions	15 minutes	Answer targeted questions for the topic
Error log update	5 minutes	Record misses, guesses, and unclear terms

120-Minute Daily Session

Block	Time	Activity
Recall and objective check	10 minutes	Review yesterday’s errors and today’s target
Concept review	30 minutes	Study documentation, examples, or learning modules
Hands-on or design practice	30 minutes	Sketch, configure, inspect, or troubleshoot a workflow
Practice questions	35 minutes	Complete a timed topic set
Missed-question review	15 minutes	Update error log and define tomorrow’s repair task

Weekly Study Distribution

Activity	Recommended Share
Objective-based concept review	25%
Hands-on Azure ML and MLOps workflow review	25%
Practice questions	25%
Missed-question analysis	15%
Mock exam and exam strategy	10%

If you are under 14 days from the exam, increase practice and missed-question review. If you are 60 or more days out, increase hands-on work.

Missed-Question Review Method

A missed question is useful only if you identify why you missed it.

Create an error log with these columns:

Field	What To Record
Date	When you missed it
Topic	Example: deployment, CI/CD, monitoring, identity, pipeline design
Question type	Recall, scenario, troubleshooting, architecture choice
Your answer	What you chose
Correct answer	What the explanation supports
Error cause	Knowledge gap, misread, weak scenario judgment, guessed, timing issue
Repair action	What you will study or practice next
Retest date	When you will answer a similar question again

Error Categories

Error Type	What It Usually Means	Repair Action
Knowledge gap	You did not know the feature or workflow	Review the official objective area and create a short summary
Confused services or assets	You mixed up jobs, components, models, endpoints, registries, or environments	Build a comparison table
Weak scenario judgment	You knew terms but chose the wrong design	Practice “best option” scenario questions
Security mistake	You ignored identity, access, secrets, or network constraints	Review security patterns and least privilege
Troubleshooting mistake	You jumped to a fix without identifying the failure point	Use a root-cause checklist
Timing issue	You understood but moved too slowly	Use timed sets and answer-elimination drills
Misread wording	You missed words such as “least,” “most secure,” “automated,” or “minimize”	Underline requirement words before answering

The 3-Pass Review Rule

For every missed question:

Pass 1: Explanation Understand why the correct answer is correct.
Pass 2: Distractors Explain why each wrong answer is wrong.
Pass 3: Transfer Write one similar scenario where the same concept would apply.

7-Day Final Review Plan

Use this plan only if you have already covered most AI-300 topics. This is a consolidation plan, not a full learning plan.

7-Day Schedule

Day	Main Goal	Study Actions
1	Baseline and triage	Take a timed mixed practice set. Build a ranked weak-area list. Review exam objectives.
2	Azure ML assets and pipelines	Review workspaces, compute, jobs, components, environments, data assets, models, and pipeline flow. Drill related questions.
3	CI/CD and MLOps automation	Review source control, build validation, pipeline triggers, artifact registration, approvals, promotion, and rollback.
4	Deployment and operations	Review online and batch deployment concepts, endpoint testing, traffic management, deployment validation, and failure recovery.
5	Monitoring, security, and governance	Review identity, access control, secrets, networking concepts, logging, alerts, drift, performance, and auditability.
6	Full timed mock	Take a timed mock exam. Review every missed or guessed question. Do not add broad new material.
7	Final light review	Review error log, key diagrams, comparison tables, and exam strategy. Stop heavy studying early.

7-Day Rules

Do not spend more than one day on any single topic unless it is your largest weakness.
Do not rebuild your entire notes system.
Do not start a large hands-on project.
Use timed practice every day.
Stop adding new material after Day 5 unless the gap is severe and frequently tested in practice.
On the final day, review only high-yield notes, mistakes, and decision patterns.

14-Day Focused Plan

Use this plan if you have Azure, ML, DevOps, or production engineering experience but need focused AI-300 exam preparation.

Week 1: Objective Coverage and Weak-Area Repair

Day	Focus	Study Tasks
1	Diagnostic and planning	Timed mixed practice. Create error log. Rank top weak areas.
2	Azure Machine Learning foundations	Review workspace structure, compute, jobs, assets, environments, models, and registries.
3	Pipeline design	Review reusable components, inputs/outputs, dependencies, reproducibility, and pipeline validation.
4	Data, model, and environment versioning	Practice lineage scenarios, asset promotion, reproducible runs, and rollback reasoning.
5	CI/CD for MLOps	Review repository structure, automated tests, pipeline execution, approvals, and deployment stages.
6	Deployment choices	Compare online, batch, blue-green style promotion, canary-style validation, and rollback concepts.
7	Timed topic review	Take two timed topic sets. Review errors deeply. Update weak-area ranking.

Week 2: Exam Conditioning and Scenario Practice

Day	Focus	Study Tasks
8	Monitoring and observability	Review logs, metrics, alerts, model performance signals, drift concepts, and incident response.
9	Security and governance	Review identity, access control, managed identities, secrets, networking constraints, approvals, and audit trails.
10	Troubleshooting	Practice failed pipeline, failed deployment, permission, environment, data, and monitoring scenarios.
11	Mixed scenario drills	Complete timed mixed sets. Focus on choosing the best answer under constraints.
12	Full mock exam	Take a full timed mock. Mark guessed questions. Review all misses.
13	Weak-area sprint	Repair the top 3 weak areas from the mock. Use short drills, not broad rereading.
14	Final review	Review error log, key decision tables, and exam-day strategy. Keep study light.

14-Day Priority Order

If time is tight, prioritize:

Deployment and operational monitoring scenarios
CI/CD and pipeline automation
Azure ML asset relationships and lifecycle
Security, identity, and governance
Troubleshooting and root-cause analysis
Responsible AI and auditability concepts

30-Day Balanced Plan

Use this path if you want a realistic schedule with time for concept review, practice questions, and hands-on reinforcement.

30-Day Structure

Phase	Days	Goal	Output
Phase 1	1 to 5	Establish baseline and Azure ML foundation	Diagnostic, notes, first weak-area list
Phase 2	6 to 12	Learn pipeline and asset lifecycle	Pipeline flow map and targeted practice results
Phase 3	13 to 19	Cover CI/CD, deployment, and operations	Deployment decision table
Phase 4	20 to 24	Cover monitoring, security, governance, troubleshooting	Root-cause checklist
Phase 5	25 to 30	Mock exams, weak-area sprint, final review	Readiness decision

Days 1 to 5: Baseline and Foundation

Day	Focus	Actions
1	Diagnostic	Take a mixed practice set. Build your error log. Review official AI-300 objectives.
2	Azure ML workspace concepts	Review workspaces, compute, jobs, experiments, environments, models, and assets.
3	Training job lifecycle	Follow the path from code and data to run output and registered model.
4	Asset relationships	Compare data assets, model assets, environment assets, components, and registries.
5	Foundation drill	Timed questions on Azure ML fundamentals. Review misses.

Days 6 to 12: Pipelines and Reproducibility

Day	Focus	Actions
6	Pipeline components	Review reusable components, inputs, outputs, and dependencies.
7	Pipeline design	Sketch a training, evaluation, and registration pipeline.
8	Reproducibility	Review versioned code, data, environments, parameters, and lineage.
9	Data and model lifecycle	Practice scenarios involving promotion, rollback, and auditability.
10	Scheduling and automation	Review when and why pipelines run automatically.
11	Pipeline troubleshooting	Diagnose failed steps, missing dependencies, bad inputs, and permission failures.
12	Timed review	Complete a timed mixed set focused on pipelines and assets.

Days 13 to 19: CI/CD, Deployment, and Release Operations

Day	Focus	Actions
13	MLOps repository structure	Review code, pipeline definitions, environment files, tests, and deployment configuration.
14	CI validation	Study what should be checked before model training or deployment.
15	CD and promotion	Review model registration, environment promotion, approvals, and staged deployment.
16	Online deployment concepts	Review endpoints, deployments, testing, traffic routing, health, and rollback reasoning.
17	Batch deployment concepts	Review batch scoring scenarios and operational considerations.
18	Release troubleshooting	Practice failed deployment, unhealthy endpoint, and version mismatch scenarios.
19	Timed mock section	Complete a timed mixed set covering CI/CD and deployment.

Days 20 to 24: Monitoring, Security, Governance, and Troubleshooting

Day	Focus	Actions
20	Monitoring fundamentals	Review logs, metrics, endpoint health, data quality, drift concepts, and alerts.
21	Model performance operations	Practice scenarios where model quality changes after deployment.
22	Identity and access	Review role-based access, managed identities, least privilege, secrets, and workspace access patterns.
23	Governance and responsible AI	Review documentation, approval workflows, auditability, explainability, and risk controls.
24	Troubleshooting drill	Use root-cause analysis on failed jobs, broken pipelines, access errors, and monitoring gaps.

Days 25 to 30: Mock Exams and Final Sprint

Day	Focus	Actions
25	Full mock 1	Take a timed mock. Mark all guessed questions.
26	Mock 1 review	Review every missed and guessed question. Group errors by topic.
27	Weak-area repair	Study the top 2 weak areas. Complete targeted drills.
28	Full mock 2 or large timed set	Simulate exam pacing. Practice answer elimination.
29	Final remediation	Review remaining high-value misses. Build final one-page checklist.
30	Light final review	Review notes and error log. Stop heavy new learning.

60/90-Day Full Preparation Path

Use this path if you are newer to Azure Machine Learning, DevOps practices, or production ML operations. The 60-day version compresses the schedule by combining review days. The 90-day version gives more time for hands-on repetition.

60/90-Day Phase Overview

Phase	60-Day Timing	90-Day Timing	Goal
1. Foundation	Days 1 to 10	Days 1 to 15	Build Azure ML and MLOps vocabulary
2. Azure ML assets and jobs	Days 11 to 20	Days 16 to 30	Understand workspace operations and asset lifecycle
3. Pipelines and reproducibility	Days 21 to 30	Days 31 to 45	Design reliable ML pipelines
4. CI/CD and deployment	Days 31 to 42	Days 46 to 65	Automate promotion and release operations
5. Monitoring, security, governance	Days 43 to 50	Days 66 to 78	Operate models safely in production
6. Exam conditioning	Days 51 to 60	Days 79 to 90	Timed mocks, weak-area repair, final readiness

Phase 1: Foundation

Focus	Study Actions
Exam orientation	Read the official AI-300 objective outline and convert it into a checklist.
MLOps concepts	Review why ML systems need versioning, automation, validation, monitoring, and governance.
Azure ML overview	Learn how workspaces, compute, jobs, environments, data, models, and endpoints relate.
DevOps basics	Review Git, pull requests, CI validation, release pipelines, secrets, approvals, and rollback concepts.
First diagnostic	Take a short mixed practice set and create your error log.

Phase 2: Azure ML Assets and Jobs

Focus	Study Actions
Workspaces and compute	Understand how ML resources are organized and used.
Jobs and experiments	Trace training execution from code submission to outputs.
Environments	Review dependency management and reproducibility.
Data assets	Review versioning, access, and lineage concepts.
Model assets and registries	Understand model registration, promotion, discovery, and reuse.
Practice	Complete targeted questions and create comparison notes for each asset type.

Phase 3: Pipelines and Reproducibility

Focus	Study Actions
Pipeline components	Study reusable steps, inputs, outputs, and dependency flow.
Training pipeline	Design a pipeline with data preparation, training, evaluation, and registration.
Reproducibility	Connect code version, data version, environment version, parameters, and outputs.
Validation	Identify where tests, metrics thresholds, and approval checks fit.
Troubleshooting	Practice diagnosing failed steps, missing outputs, permission errors, and environment issues.

Phase 4: CI/CD and Deployment

Focus	Study Actions
Repository design	Know where code, pipeline definitions, tests, infrastructure, and deployment files belong.
CI for ML	Review linting, unit tests, component tests, security checks, and pipeline validation.
CD for ML	Review model registration, deployment, smoke testing, approval, promotion, and rollback.
Endpoint deployment	Practice online endpoint scenarios, traffic shifting concepts, validation, and health checks.
Batch scoring	Practice scenarios where batch deployment is more appropriate than real-time serving.
Release decisions	Build a table for choosing deployment and rollback strategies.

Phase 5: Monitoring, Security, Governance, and Responsible AI

Focus	Study Actions
Monitoring	Review logs, metrics, endpoint health, model performance signals, drift concepts, and alerting.
Incident response	Practice diagnosing degraded model quality or failed scoring.
Identity and access	Review least privilege, managed identities, role-based access, secrets, and workspace access boundaries.
Network and data protection	Review secure access patterns at a conceptual level.
Governance	Review auditability, documentation, approval workflows, model cards or similar documentation practices, and traceability.
Responsible AI	Review explainability, fairness, human review, and operational risk controls.

Phase 6: Exam Conditioning

Activity	Frequency	Purpose
Timed topic sets	3 to 5 per week	Improve speed and expose weak areas
Full timed mock	1 per week in final phase	Practice endurance and pacing
Error log review	After every practice session	Convert mistakes into study tasks
Weak-area sprint	2 to 3 times per week	Repair recurring gaps
Final review notes	Last 7 days	Consolidate decision rules

Hands-On Review Targets

AI-300 is easier to prepare for when you can reason through real MLOps workflows. You do not need a large project; use small, repeatable exercises.

Hands-On Target	What To Practice	What You Should Be Able To Explain
Training job flow	Submit or inspect a training job workflow	Where code, data, environment, compute, and outputs fit
Pipeline structure	Review a pipeline with multiple components	How inputs and outputs move between steps
Environment reproducibility	Compare environment versions or dependency definitions	Why dependency control matters
Model registration	Trace model output to registered model	How promotion and rollback become possible
Deployment validation	Review endpoint testing and deployment health	How to decide whether a deployment is safe
Monitoring setup	Inspect logs, metrics, and alert concepts	How operations teams detect failures
Security review	Identify identity and secret handling patterns	How least privilege applies to MLOps

Optional Command-Oriented Practice

Use command patterns only to reinforce concepts. Do not memorize commands without understanding the workflow.

## Submit a job from a definition file
az ml job create \
  --file job.yml \
  --resource-group <resource-group> \
  --workspace-name <workspace>

## Create or update an Azure ML asset from a definition file
az ml model create \
  --file model.yml \
  --resource-group <resource-group> \
  --workspace-name <workspace>

## Inspect an endpoint
az ml online-endpoint show \
  --name <endpoint-name> \
  --resource-group <resource-group> \
  --workspace-name <workspace>

For exam preparation, focus on what each operation accomplishes, what permissions it requires, what can fail, and how it fits into an automated MLOps workflow.

Scenario Decision Tables

CI vs CD vs Monitoring

Scenario	Best Study Lens
Code changed and tests must run before training	CI
A trained model must be registered and promoted	CD
A deployment must be validated before receiving more traffic	CD and release governance
Endpoint latency or errors increased	Monitoring and incident response
Model predictions degrade after data changes	Monitoring, drift, and retraining workflow
A pipeline step fails because it cannot access data	Security, identity, data access, and troubleshooting
A previous model version must be restored	Versioning, registry, deployment rollback

Deployment Choice Review

Requirement	Think About
Real-time prediction needed	Online endpoint patterns
Large scheduled scoring job	Batch scoring patterns
Need to test a new version safely	Staged deployment, validation, traffic control, rollback
Need audit trail for production model	Model version, approval records, lineage, deployment history
Need repeatable deployment	Infrastructure/configuration as code and automated release
Need secure service-to-service access	Managed identity and least privilege

Troubleshooting Review

Symptom	Possible Areas To Check
Training job fails immediately	Compute, environment, permissions, job definition, missing inputs
Pipeline step cannot find data	Data asset version, path/reference, identity, access control
Model registration fails	Output path, model format, permissions, registration configuration
Deployment unhealthy	Environment mismatch, scoring code, dependencies, resource configuration, logs
Endpoint returns errors	Request schema, scoring script, model load failure, authentication, logs
Monitoring gap	Missing metrics, logging configuration, alert rules, data collection, access
Unexpected model behavior	Data drift, feature changes, training-serving skew, model version mismatch

When To Use Timed Mock Exams

Timed mock exams should be used after you have enough coverage to learn from the results.

Preparation Stage	Mock Exam Use
Beginning	Use a short diagnostic set, not a full mock, unless you already have experience
60/90-day plan	Start full mocks in the final 2 to 3 weeks
30-day plan	Take full mocks around Days 25 and 28
14-day plan	Take one full mock around Day 12
7-day plan	Take one full mock on Day 6, or earlier if you need more remediation time

How To Review a Mock Exam

Review Step	Action
Score summary	Identify the lowest-performing topic areas
Missed questions	Review every miss using the 3-pass review rule
Guessed correct questions	Treat these as weak areas
Slow questions	Identify topics that take too long
Repeated mistakes	Create a focused repair task
Next practice set	Build it from your top 2 weak areas

Do not take repeated mock exams without reviewing them. Review time should be at least as long as test-taking time.

Final-Week Rules

Use the final week to improve exam readiness, not to rebuild your knowledge from scratch.

What To Do

Review your error log daily.
Drill the top 3 weak areas.
Practice timed question sets.
Revisit deployment, CI/CD, monitoring, security, and troubleshooting scenarios.
Rehearse answer-elimination strategy.
Sleep enough to avoid careless scenario mistakes.

What To Stop Doing

Stop	Why
Starting broad new courses	Too much passive input too late
Taking mocks without review	Low learning value
Memorizing isolated terms only	AI-300 scenarios require workflow judgment
Ignoring guessed questions	Guesses hide weak understanding
Studying only strong topics	Comfort review does not improve readiness
Heavy study the night before	Increases fatigue and misreading risk

When To Stop Adding New Material

Time Remaining	Rule
7 days	Add only material tied to repeated missed questions
3 days	Stop broad new learning; repair only high-frequency gaps
1 day	Review notes, error log, and decision tables only
Exam day	Do not cram. Use light recall and pacing strategy

Exam-Readiness Checks

You are more likely to be ready for AI-300 when you can do the following without notes.

Readiness Area	Check
Azure ML lifecycle	Explain how data, code, environment, job, model, endpoint, and monitoring connect
Pipeline design	Design a repeatable training and evaluation pipeline
Reproducibility	Identify which versions must be captured to reproduce a model
CI/CD	Separate build validation, training automation, deployment, approval, and rollback
Deployment	Choose between online and batch approaches for a scenario
Monitoring	Explain how to detect endpoint, data, or model performance problems
Security	Apply least privilege, managed identity concepts, and secure secret handling
Governance	Identify where auditability, approval, documentation, and responsible AI checks fit
Troubleshooting	Narrow a failure to data, code, environment, identity, deployment, or monitoring
Timing	Complete practice sets within time without rushing the final questions

Readiness Decision

If This Is True	Recommended Action
You consistently miss the same topic	Delay broad review and run a weak-area sprint
You understand explanations but miss timed questions	Practice timed sets and answer elimination
You score well but guess often	Review guessed questions as misses
You have weak hands-on understanding	Walk through small Azure ML workflows and diagrams
You are strong in Azure but weak in ML operations	Focus on model lifecycle, monitoring, drift, and release governance
You are strong in ML but weak in Azure	Focus on Azure ML assets, identity, endpoints, and operational tooling

Final 48-Hour Checklist

Time	Action
48 hours before	Review mock results and top weak areas
36 hours before	Complete one final targeted timed set
24 hours before	Review error log, decision tables, and lifecycle diagrams
Night before	Stop heavy study. Prepare exam logistics. Rest.
Exam day	Do light recall only. Read each scenario carefully. Manage time.

Practical Next Step

Choose your plan based on the time you have left, then complete a diagnostic practice set before studying more. Use the results to build your AI-300 weak-area list, and spend each study session on one concrete outcome: learn the concept, apply it to an MLOps scenario, answer timed questions, and review every miss.

Quick Review

Exam Blueprint