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This certification validates your ability to design secure, scalable, and resilient cloud and hybrid solutions on Microsoft Azure. It emphasizes translating business requirements into architecture across compute, network, storage, identity, and governance. You’ll demonstrate fluency in the Azure Well-Architected Framework and Cloud Adoption Framework, ensuring alignment with enterprise goals. Ideal for professionals leading digital transformation, this credential confirms your ability to integrate AI/ML, DevOps, business continuity, and security into cloud-native and hybrid deployments.
PL-600 certifies your ability to lead the design of end-to-end solutions using Microsoft Power Platform, Dynamics 365, and Microsoft cloud services. You’ll demonstrate expertise in governance, security, integration, and change management. This certification is ideal for professionals who guide enterprise low-code strategies, collaborate with stakeholders, and ensure scalable, maintainable solutions. It reflects your ability to align technical architecture with business outcomes and lead cross-functional teams through successful implementations.
GCP-ACE This certification validates your ability to design, develop, and manage secure, scalable cloud solutions on Google Cloud. It covers architecture, networking, security, compliance, and business alignment. You’ll demonstrate fluency in Google Cloud’s infrastructure, data services, and AI/ML capabilities. Ideal for architects leading cloud strategy and transformation, this credential includes case studies and scenario-based assessments.
MLS-C01 This certification validates deep expertise in building, training, and deploying ML models on AWS. It covers SageMaker, model tuning, feature engineering, and real-world ML pipelines. Ideal for data scientists and ML engineers, it demonstrates your ability to solve complex business problems using AWS AI/ML services.
This certification demonstrates strategic fluency in generative AI using Google Cloud. It covers AI concepts, prompt engineering, LLMs, responsible AI, and enterprise-grade tooling. Ideal for executives, consultants, and decision-makers, it positions you to lead AI-powered innovation and align emerging capabilities with business transformation.
AI-102 certifies your ability to design, build, and deploy AI solutions using Azure Cognitive Services, Azure Bot Service, and Azure Machine Learning. You’ll demonstrate skills in natural language processing, computer vision, conversational AI, and responsible AI practices. This certification is ideal for engineers integrating intelligent features into enterprise applications and operationalizing AI at scale within Microsoft’s cloud ecosystem.
"EARLY ADOPTER"
MLA-C01 This certification validates your ability to implement and operationalize ML workloads on AWS. It covers data preparation, model development, deployment, orchestration, and monitoring. Ideal for MLOps engineers and backend developers, it demonstrates proficiency in SageMaker and CI/CD for AI/ML.
PL-200 This certification validates your ability to configure Power Platform apps, automate workflows, and integrate Microsoft services. You’ll demonstrate skills in data modeling, user experience design, and solution delivery using Power Apps, Power Automate, Power BI, and Dataverse. Ideal for consultants who translate business needs into scalable low-code solutions, this credential reflects your ability to drive digital transformation through automation and app innovation.
"EARLY ADOPTER"
AIF-C01 certifies foundational knowledge of AI/ML concepts and AWS services. It’s designed for professionals who use—but don’t necessarily build—AI solutions. Ideal for business analysts, product managers, and consultants, it covers responsible AI, use cases, and AWS tools like Bedrock and SageMaker.
PL-100 It validates the ability to build apps, automate workflows, and analyze data using Power Platform tools—without extensive coding. It is ideal for business users creating solutions to streamline operations. Its legacy continues through Microsoft’s Applied Skills credentials.
This certification validates foundational knowledge of cloud computing and Google Cloud services. It covers digital transformation, data analytics, AI/ML, infrastructure modernization, and security. Designed for non-technical professionals, it’s ideal for business leaders aligning cloud capabilities with organizational goals.
GCP-ACE certifies hands-on skills in deploying, managing, and securing Google Cloud infrastructure. It covers compute, storage, networking, IAM, AI and billing. Ideal for early-career professionals, this certification demonstrates your ability to support cloud operations and implement foundational services across multiple projects.
🔵 What are Microsoft AI Agents?
Microsoft AI Agents are specialized, task-driven assistants embedded across the Microsoft ecosystem — from Microsoft 365 to Azure. These agents combine generative AI, enterprise data, and workflow automation to help organizations streamline operations, enhance decision-making, and scale productivity.
🔑 Key Features
Copilot + Agents Architecture Microsoft 365 Copilot acts as the interface, while AI agents perform autonomous or semi-autonomous tasks — from drafting documents to managing workflows.
Multi-Agent Orchestration Azure AI Agent Service enables developers to build and deploy agents that collaborate, reason, and act across systems using tools like Semantic Kernel and AutoGen.
Enterprise Integration Agents connect to Microsoft Graph, SharePoint, Dynamics 365, and Power Platform. They can also trigger actions via Azure Functions, Logic Apps, and OpenAPI tools.
Low-Code Agent Creation Copilot Studio allows business users to build agents using natural language and Power Automate — no coding required.
Security & Compliance Agents inherit Microsoft’s enterprise-grade security stack: Azure AD, Purview, Defender, and full auditability. Data never leaves the tenant, and agents respect role-based access.
🧩 Use Cases:
🟡 What is Amazon Q?
Amazon Q is AWS’s generative AI platform designed to accelerate enterprise productivity through intelligent, task-oriented agents. Built on Amazon Bedrock, Amazon Q combines secure access to enterprise data, advanced reasoning, and seamless integration with AWS and third-party systems to help employees automate complex workflows and make faster decisions.
🔑 Key Features:
Multimodal Intelligence Amazon Q leverages foundation models like Claude, Titan, and Cohere to support multimodal tasks — including text, code, and structured data — across business and developer workflows.
Enterprise Data Synthesis Q connects to over 50 enterprise systems (e.g., Salesforce, ServiceNow, Atlassian, Microsoft Exchange) to unify structured and unstructured data. It enables natural language querying, summarization, and action-taking across disparate sources.
Developer & Business Agents
Agentic Workflows Supports multi-step task planning, execution, and coordination through declarative agent definitions. Agents can be embedded in apps like QuickSight, Connect, and AWS Supply Chain.
Security & Governance Built with AWS-grade security: IAM, VPC, encryption, and role-based access. Q respects enterprise permissions and includes guardrails for responsible AI usage.
🧩 Use Cases:
Google Agentspace is a powerful platform designed to enhance enterprise productivity by leveraging AI agents. These agents combine advanced reasoning, Google-quality search, and enterprise data to provide employees with a comprehensive tool for accomplishing complex tasks. Google Agentspace integrates various data sources, enabling seamless access to information across the organization.
Key Features:
Multimodal Assistance
Google Agentspace and google ai agents utilizes models like Gemini, Imagen, and Veo to offer multimodal assistance. This means it can understand and process images, text, and videos, providing a versatile tool for various tasks.
Information Discovery
Agentspace acts as a central source of enterprise truth, offering a company-branded multimodal search agent. It can handle both unstructured data (e.g., documents, emails) and structured data (e.g., tables), providing conversational assistance, answering complex questions, and making proactive suggestions.
Pre-built Connectors
The platform includes pre-built connectors for commonly used third-party applications such as Confluence, Google Drive, Jira, Microsoft Sharepoint, and ServiceNow. This feature breaks down data silos and allows employees to access and query relevant data sources easily.
Custom AI Agents
Google Agentspace enables the creation of custom AI agents that apply generative AI contextually. These agents can automate business functions, conduct research, draft content, and automate repetitive tasks, including multi-step workflows.
Security and Privacy
Built on Google Cloud’s secure-by-design infrastructure, Google Agentspace ensures data security with features like role-based access controls, encryption, and compliance with various security standards. It also includes responsible AI tools for evaluation and content moderation.
Use Cases:
Marketing
Generate high-quality marketing content, analyze engagement results, and automate repetitive tasks like weekly reporting on campaign performance1.
HR
Streamline onboarding processes, even for complex tasks like 401k selection, and enhance the employee experience.
Software Engineering
Identify and resolve bugs proactively, accelerating deployment cycles and improving efficiency.
Business Analysis
Uncover industry trends and create data-driven presentations with AI-generated insights.
The Microsoft Scenario Library is a collection of industry-specific use cases designed to showcase how Microsoft technologies can solve real-world challenges. It provides detailed scenarios, best practices, and implementation guidance to help organizations leverage AI, cloud computing, and other digital solutions effectively across various sectors.
The Microsoft Scenario Library covers a variety of industries, helping organizations leverage AI and digital solutions effectively. Here are some key industries included:
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