A.5 Architecture Design

1.1 Identify and describe core AI architecture components, such as hardware accelerators, cloud AI services, and data pipelines, in enterprise AI systems.

Assessed by Evaluation of architecture diagrams and component inventory, assessed for correct identification, description, and contextual placement of hardware accelerators, cloud AI services, and data pipelines in an enterprise AI system.

1.2 Apply standard AI design patterns and implement AI modules using Python frameworks, pre-trained models, or microservices architectures in guided projects.

Assessed by Grading of AI module code and review of module documentation, assessed for appropriate use of standard AI design patterns, Python frameworks, pre-trained models, or microservices architectures in a guided project.

1.3 Evaluate AI risks, ethical considerations, and adherence to policies by applying standardized checklists and reflection exercises in lab or project scenarios.

Assessed by Assessment of risk checklist and ethics report, using a standardized rubric to verify identification of AI risks, ethical considerations, and adherence to relevant policies in a lab or project scenario.

1.4 Integrate AI components into simple enterprise systems using APIs, cloud services, or modular pipelines in partially unpredictable business contexts.

Assessed by Evaluation of integrated prototype and deployment report, assessed for correct use of APIs, cloud services, or modular pipelines, and for evidence of interoperability in a simple enterprise system.

1.1 Identify and describe core AI architecture components, such as hardware accelerators, cloud AI services, and data pipelines, in enterprise AI systems.

Assessed by Evaluation of architecture diagrams and component inventory review, assessed for correct identification and description of hardware accelerators, cloud AI services, data pipelines, and their role in enterprise AI systems.

1.2 Apply standard AI design patterns and implement AI modules using Python frameworks, pre-trained models, or microservices architectures in guided projects.

Assessed by Grading of AI module code and review of module documentation, assessed for application of standard AI design patterns and implementation using Python frameworks, pre-trained models, or microservices architectures in guided projects.

1.3 Evaluate AI risks, ethical considerations, and adherence to policies by applying standardized checklists and reflection exercises in lab or project scenarios.

Assessed by Assessment of risk checklist and ethics report evaluation, assessed for identification of AI risks, ethical considerations, and adherence to policies using standardized checklists and reflection exercises.

1.4 Integrate AI components into simple enterprise systems using APIs, cloud services, or modular pipelines in partially unpredictable business contexts.

Assessed by Evaluation of integrated prototype and deployment report review, assessed for integration of AI components using APIs, cloud services, or modular pipelines in partially unpredictable business contexts.

1.1 Identify and describe core AI architecture components, such as hardware accelerators, cloud AI services, and data pipelines, in enterprise AI systems.

Assessed by Evaluation of architecture diagrams and component inventory review, assessed for correct identification and description of hardware accelerators, cloud AI services, data pipelines, and their role in enterprise AI systems.

1.2 Apply standard AI design patterns and implement AI modules using Python frameworks, pre-trained models, or microservices architectures in guided projects.

Assessed by Grading of AI module code and review of module documentation, assessed for application of standard AI design patterns and implementation using Python frameworks, pre-trained models, or microservices architectures in guided projects.

1.3 Evaluate AI risks, ethical considerations, and adherence to policies by applying standardized checklists and reflection exercises in lab or project scenarios.

Assessed by Assessment of risk checklist and ethics report evaluation, assessed for identification of AI risks, ethical considerations, and adherence to policies using standardized checklists and reflection exercises.

1.4 Integrate AI components into simple enterprise systems using APIs, cloud services, or modular pipelines in partially unpredictable business contexts.

Assessed by Evaluation of integrated prototype and deployment report review, assessed for integration of AI components using APIs, cloud services, or modular pipelines in partially unpredictable business contexts.

2.1 Analyse interdependencies among multiple AI systems and enterprise platforms by mapping data flows, service interactions, and integration points in real-world deployments.

Assessed by Review of dependency diagrams and analysis report evaluation, assessed for accurate mapping of data flows, service interactions, integration points, and interdependencies across multiple AI systems and enterprise platforms.

2.2 Design integrated AI solutions ensuring scalability, security, and policy compliance using enterprise-grade frameworks and architecture standards.

Assessed by Assessment of architecture blueprint, design document grading, and prototype/simulation evaluation, assessed for scalability, security, policy compliance, and appropriate use of enterprise-grade frameworks and architecture standards.

2.3 Coordinate a team to deploy AI architectures in realistic, open-ended projects by assigning roles, tracking progress, and resolving integration issues in enterprise settings.

Assessed by Evaluation of team deployment plan and observation of deployment report outcomes, assessed for role assignment, progress tracking, issue resolution, and coordination of AI architecture deployment in an enterprise setting.

2.4 Evaluate AI risks, biases, and ethical implications in multi-factor decision-making using scenario analyses, audits, or simulation tools.

Assessed by Assessment of risk & bias report and ethics evaluation report review, assessed for evaluation of AI risks, biases, and ethical implications using scenario analyses, audits, or simulation tools.

2.5 Document and present AI architecture solutions for diverse stakeholders through reports, visual diagrams, and presentations tailored to technical and non-technical audiences.

Assessed by Grading of technical documentation and presentation slides review, assessed for completeness, technical accuracy, visual clarity, and suitability for both technical and non- technical stakeholders.

2.6 Optimise AI deployment and operational performance by monitoring system metrics, troubleshooting integration issues, and applying iterative improvements in enterprise projects.

Assessed by Evaluation of performance report and review of optimized system documentation, assessed for use of system metrics, troubleshooting of integration issues, and evidence of iterative operational improvements.

3.1 Analyse interdependencies among multiple AI systems and enterprise platforms by mapping data flows, service interactions, and integration points in real-world deployments.

Assessed by Review of dependency diagrams and Analysis report evaluation assessing mapping of data flows, service interactions, and integration points among multiple AI systems and enterprise platforms.

3.2 Design integrated AI solutions ensuring scalability, security, and policy compliance using enterprise-grade frameworks and architecture standards.

Assessed by Assessment of architecture blueprint, Design document grading, and Prototype/simulation evaluation assessing integrated AI solution design for scalability, security, and policy compliance.

3.3 Coordinate a team to deploy AI architectures in realistic, open-ended projects by assigning roles, tracking progress, and resolving integration issues in enterprise settings.

Assessed by Evaluation of team deployment plan and Observation of deployment report outcomes assessing team coordination, role assignment, progress tracking, and resolution of integration issues.

3.4 Evaluate AI risks, biases, and ethical implications in multi-factor decision-making using scenario analyses, audits, or simulation tools.

Assessed by Assessment of risk & bias report and Ethics evaluation report review assessing AI risks, biases, and ethical implications using scenario analyses, audits, or simulation tools.

3.5 Document and present AI architecture solutions for diverse stakeholders through reports, visual diagrams, and presentations tailored to technical and non-technical audiences.

Assessed by Grading of technical documentation and Presentation slides review assessing clarity, completeness, and tailoring of AI architecture communication to technical and non-technical stakeholders.

3.6 Optimise AI deployment and operational performance by monitoring system metrics, troubleshooting integration issues, and applying iterative improvements in enterprise projects.

Assessed by Evaluation of performance report and Review of optimized system documentation assessing metric monitoring, troubleshooting of integration issues, and iterative performance improvements.

3.1 Analyse interdependencies among multiple AI systems and enterprise platforms by mapping data flows, service interactions, and integration points in real-world deployments.

3.2 Design integrated AI solutions ensuring scalability, security, and policy compliance using enterprise-grade frameworks and architecture standards.

3.3 Coordinate a team to deploy AI architectures in realistic, open-ended projects by assigning roles, tracking progress, and resolving integration issues in enterprise settings.

3.4 Evaluate AI risks, biases, and ethical implications in multi-factor decision-making using scenario analyses, audits, or simulation tools.

3.5 Document and present AI architecture solutions for diverse stakeholders through reports, visual diagrams, and presentations tailored to technical and non-technical audiences.

3.6 Optimise AI deployment and operational performance by monitoring system metrics, troubleshooting integration issues, and applying iterative improvements in enterprise projects.