Path B Cloud AI Certification Guide

Contents

Practical Notebook recommendation

Why a cloud credential can fit Path B.

The credential is useful when it names an ecosystem you already use and organizes the next body of knowledge. It is not a substitute for the application evidence developed in the book.

It follows real application work.

Path B begins with an application contract, retrieval or model integration, evaluation cases, and explicit failure behavior.

It narrows the cloud decision.

One provider becomes primary because of target roles and project constraints, not because its certification appeared first.

It complements portfolio proof.

The credential can validate provider knowledge while your project shows decisions, evaluation, security, deployment, and limitations.

It creates a current milestone.

Once the provider is chosen, the live exam scope can turn a broad cloud goal into a specific preparation target.

Path B decision gate

Choose the cloud before the credential.

A current cloud credential is worth reviewing only when all four statements below are true.

You have completed a relevant application artifact.
You have selected one primary cloud ecosystem.
Your target roles use or value that ecosystem.
You can explain evaluation, security, latency, deployment, and cost tradeoffs.

Current provider routes

Match the credential to the ecosystem already chosen.

These are not ranked against one another. Each route is relevant only when its provider already matches your Path B project and target work.

Amazon Web Services

AWS Certified Machine Learning Engineer (Associate)

Best aligned when your Path B application and target roles already use AWS services for machine learning engineering.

Scope: Build, operationalize, deploy, and maintain machine-learning workloads on AWS.
Experience signal: AWS describes the intended candidate as having at least one year of experience with SageMaker and other AWS ML engineering services.
Current pricing: Current standard exam page lists US$150. The MLA-C02 beta announcement lists US$75 for the beta exam.

View the Amazon Web Services credential Official provider page. Verify the current exam version and regional terms before scheduling.

Google Cloud

Google Cloud Professional Machine Learning Engineer

Best aligned when your Path B evidence already uses Google Cloud and the work extends into model delivery, pipelines, monitoring, and governance.

Scope: Build, evaluate, productionize, optimize, and monitor AI solutions using Google Cloud.
Experience signal: Google recommends more than three years of industry experience, including at least one year designing and managing Google Cloud solutions.
Current pricing: US$200 plus tax where applicable.

View the Google Cloud credential Official provider page. Verify the current exam version and regional terms before scheduling.

Microsoft

Microsoft Certified: Azure AI Apps and Agents Developer Associate

Best aligned when your Path B application uses Azure and Microsoft Foundry for agents, retrieval, evaluation, security, or multimodal AI workflows.

Scope: Design, develop, manage, and deploy AI applications and agents using Azure and Microsoft Foundry.
Experience signal: Microsoft positions the credential at intermediate level and expects experience developing applications with Python plus familiarity with Azure services.
Current pricing: Pricing varies by the country or region where the exam is proctored.

View the Microsoft credential Official provider page. Verify the current exam version and regional terms before scheduling.

30-second direction check

Is it time to review a credential?

This check applies the decision sequence from the book. It does not test eligibility, and nothing is submitted.

Which cloud already appears in your project or target roles?

AWSGoogle CloudMicrosoftNot chosen

Have you built and evaluated a relevant AI application artifact?

YesNot yet

Do the roles you are targeting use this ecosystem?

YesNot confirmed

Answer all three questions to see the next credible step. Nothing is submitted.

Project evidence first

What should exist before you schedule.

Application contract

Define the user, supported questions, information boundary, output contract, and failure behavior.

Evaluation set

Keep representative cases for grounding, citations, structured outputs, unsupported requests, and failure analysis.

Security boundary

Separate authentication from authorization and document how data, secrets, tools, and retrieved sources are controlled.

Delivery record

Make the application reproducible and record latency, cost, deployment choices, operational limits, and known failures.

Open the Project 2 workspace

Optional hands-on practice

Need guided cloud labs before the credential?

KodeKloud offers guided labs across cloud, Linux, containers, Kubernetes, and related operational skills. Use it to close a specific practice gap after choosing the ecosystem and evidence you need.

Review KodeKloud hands-on labs External training resource. Compare the current catalog, price, and lab coverage with your selected cloud. Training is separate from the provider credentials above.

After the cloud decision

Turn the selected route into a focused plan.

Keep project work in the schedule. Credential preparation should reinforce the application evidence, not replace it.

01Build the Path B plan

Adapt the twelve-week planner to your current application, cloud ecosystem, and credential decision.

02Strengthen Project 2

Return to the application contract, evaluation set, security boundary, and delivery evidence.

Primary sources

Verify the live credential before you register.

Exam names, versions, pricing, delivery, and provider policies can change. These official pages were used for the current review.