Leading Digital Transformation with AI

Describe the key components of a data maturity model for AI readiness (data quality, governance, infrastructure, and organizational skills).

Apply a data maturity framework to evaluate an organization's AI readiness based on a given scenario.

Compare data architecture patterns (Data Lake, Data Warehouse, Lakehouse) for their suitability in supporting AI workloads.

Explain the role of feature stores and feature engineering in production ML systems.

Distinguish between batch and real-time data pipelines for AI and select appropriate patterns for given use cases.

Specify data governance principles that enable AI initiatives while maintaining quality and compliance.

Establish a framework for evaluating enterprise AI/ML platforms based on key architectural concerns (data integration, model development, MLOps, serving, cost, governance).

Apply the evaluation framework to compare major cloud AI/ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and identify key trade-offs.

Evaluate build vs. buy decisions for AI/ML infrastructure components based on enterprise strategy and constraints.

Explain the key components and goals of an MLOps framework, including CI/CD for ML, model registry, and monitoring.

Compare and select model serving patterns (e.g., real-time, batch, streaming) based on enterprise use case requirements.

Design CI/CD pipelines for ML that automate model training, validation, versioning, and deployment strategies.

Design for scalability, high availability, and low latency in a model serving architecture using cloud-native patterns.

Explain the architecture of transformer-based models at a level sufficient for enterprise design decisions.

Evaluate when to use pre-trained, fine-tuned, or custom-trained models for a given enterprise use case.

Describe the components of a Retrieval-Augmented Generation (RAG) architecture and the data flow between stages.

Design a RAG architecture for a given enterprise use case, specifying choices for vector store, embedding model, and LLM.

Architect a system for managing and versioning prompts in enterprise LLM applications.

Architect an orchestration layer for a multi-step, tool-using LLM-powered workflow.

Assess cost, latency, and quality trade-offs across LLM deployment options (API-based, self-hosted, hybrid).

Compare synchronous, asynchronous, and batch integration patterns for AI model consumption.

Design an integration architecture for embedding AI capabilities into an enterprise workflow, selecting appropriate patterns.

Plan incremental migration paths for introducing AI into legacy system landscapes.

Design human-in-the-loop architectures for AI systems requiring oversight and approval workflows.

Architect multi-model orchestration patterns (e.g., chaining, routing, ensembles) for complex AI workflows.

Design feedback loops that capture user interactions to continuously improve model performance.

Define fallback and graceful degradation strategies for AI-dependent systems.

Describe the key pillars of an AI governance framework: accountability, transparency, and auditability.

Specify model explainability requirements and integrate them into solution architecture.

Identify bias risks in AI system design and define architectural mitigations.

Architect audit trails and lineage tracking for AI-driven decision-making systems.

Map architectural responsibilities for model accountability within an enterprise context.

Translate regulatory requirements (e.g., EU AI Act, GDPR) into concrete architectural design patterns for AI systems.

Identify AI-specific security threats including adversarial attacks, data poisoning, and model extraction.

Design security controls for protecting training data and model artifacts throughout their lifecycle.

Design security controls for AI inference endpoints, including authentication, rate limiting, and input validation.

Evaluate privacy-preserving approaches (e.g., differential privacy, federated learning) and their architectural implications.

Define access control and authorization patterns specific to AI services, including model-level permissions and inference API governance.

Describe the unique cost structures and value drivers of AI initiatives compared to traditional IT projects.

Apply an ROI framework and define success metrics for a given AI transformation initiative.

Identify and prioritize AI use cases based on business value, technical feasibility, and data readiness.

Develop an AI adoption roadmap aligned with enterprise architecture strategy and organizational capabilities.

Communicate AI strategies, trade-offs, and risk profiles to executive stakeholders effectively.

Formulate an AI ecosystem strategy balancing build vs. buy, open-source vs. proprietary, and vendor approaches.

Lead architecture reviews for AI-intensive solutions using a structured evaluation framework.