AI Aware Solutions Architects requires a wealth of various skill sets, which are covered on the exam. Â
The certification exam will test candidates on the following exam objectives.
- AI/ML FundamentalsÂ
- Identifying and Qualifying OpportunitiesÂ
- Solution Development and ProposalÂ
- Closing and Relationship ManagementÂ
Certified Cloud AI Solutions Architect (CCASA)
Exam Objectives
Domain 1: AI/ML Fundamentals (20%)
1.1 Core Concepts:
• Define AI, ML, and Deep Learning (DL) and their key differences.
• Explain common AI/ML use cases and applications across various industries.
• Describe different types of machine learning (supervised, unsupervised, reinforcement learning).
• Define and differentiate between Generative AI, Predictive AI, Agentic AI, and others.
1.2 AI/ML Value Proposition:
• Articulate the benefits of AI/ML solutions for businesses.
• Identify potential challenges and limitations of AI/ML adoption.
• Explain the importance of data in AI/ML solutions.
1.3 AI/ML Landscape:
• Identify key players in the AI/ML market (cloud providers, technology vendors, etc.).
• Recognize standard AI/ML tools, platforms, and frameworks.
• Stay updated on emerging trends and advancements in AI/ML.
Domain 2: Identifying and Qualifying Opportunities (25%)
2.1 Lead Generation:
• Identify potential clients and industries benefiting from AI/ML solutions.
• Develop effective lead generation strategies for AI/ML services.
• Utilize various channels (e.g., online platforms, networking events, referrals) to reach potential clients.
2.2 Needs Analysis:
• Conduct effective discovery calls and meetings to understand client needs and pain points.
• Identify opportunities where AI/ML can provide value and address business challenges.
• Analyze client data and infrastructure to assess feasibility and requirements for AI/ML solutions.
2.3 Qualifying Leads:
• Determine the client's readiness and commitment to AI/ML adoption.
• Assess the client's budget and resources for implementing AI/ML solutions.
• Prioritize leads based on potential value and likelihood of successful engagement.
2.4. Identify Use Cases
Domain 3: Solution Development and Proposal (30%)
3.1 Solution Design:
• Design AI/ML solutions tailored to specific client needs and objectives.
• Select appropriate AI/ML techniques and technologies for the proposed solution.
• Define data requirements, model development, and deployment strategies.
3.2 Value Proposition and ROI:
• Clearly articulate the value proposition of the proposed AI/ML solution.
• Develop a compelling business case and demonstrate potential ROI.
• Address client concerns and objections regarding AI/ML adoption.
3.3 Proposal Development:
* Create professional and persuasive proposals outlining the solution, timeline, and pricing.
* Effectively communicate technical details clearly and concisely.
* Negotiate terms and conditions to reach mutually beneficial agreements.
Domain 4: Closing and Relationship Management (25%)
4.1 Closing Techniques:
• Utilize effective closing techniques to secure deals and win contracts.
• Address client concerns and negotiate final terms.
• Manage the transition from sales to project implementation.
4.2 Building Long-Term Relationships:
• Develop strategies for building and maintaining long-term client relationships.
• Provide ongoing support and address client needs after project completion.
• Seek opportunities for upselling and cross-selling additional AI/ML services.
4.3 Ethical Considerations:
• Understand and address ethical considerations in selling AI/ML solutions.
• Promote responsible AI practices and ensure transparency with clients.
• Maintain client confidentiality and data privacy.
END of Objectives
