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| Section | Weight | Objectives |
|---|---|---|
| AI Fundamentals and Strategy | 15% | - AI Concepts and Terminology - AI Ethics and Governance Frameworks - AI Business Strategy Alignment |
| AI Team Leadership and Management | 20% | - Conflict Resolution in AI Projects - Cross-functional Collaboration - Building AI Teams - Talent Management and Development |
| AI Project Lifecycle Management | 25% | - Data Preparation and Management - Model Development and Testing - Deployment and Operations (MLOps) - AI Development Methodology (CRISP-DM, Agile) - Monitoring and Maintenance |
| AI Program Evaluation and Optimization | 10% | - KPI and Success Metrics - Continuous Improvement - Performance Measurement |
| Risk Management and Compliance | 10% | - Regulatory Compliance (GDPR, CCPA) - AI Risk Identification and Assessment - Security Considerations for AI |
| AI Program Planning | 20% | - AI Project Scoping and Feasibility Analysis - Stakeholder Identification and Analysis - Resource Planning and Budgeting - Requirements Gathering for AI Projects |
Question 1
Michael Turner, an Enterprise AI Program Lead at a multinational technology company, structured the initial rollout of a new AI productivity platform by enabling it first within individual departments. Each function received customized training and ownership for adoption. However, within weeks, teams reported inconsistent workflows, handoff delays between departments, and confusion when collaborating on shared processes that spanned multiple functions. These issues slowed enterprise-wide adoption despite strong uptake within individual teams. Based on this outcome, which rollout sequencing approach most directly contributed to the problem encountered?
A. Use Case
B. Department/Function
C. Hybrid Approach
D. Geography/Region
Question 2
You are the AI Portfolio Owner for a manufacturer developing a new line of industrial IoT sensors. The product requirements mandate that the AI system must operate with ultra-low latency and function reliably in environments with intermittent internet connectivity. Additionally, strict client compliance rules prohibit the transmission of raw telemetry outside the local environment. Which emerging AI trend must you prioritize in the architectural roadmap to ensure processing occurs at the source of data generation?
A. Domain-Specific AI
B. Edge AI
C. Multimodal AI
D. Explainable AI XAI
Question 3
An organization is scaling multiple AI initiatives across various departments. Data flows smoothly into the platform and passes initial validation checks. However, during audit reviews, the team struggles to trace how AI outputs connect to the original enterprise data after undergoing multiple transformations. While the data quality remains satisfactory, there are inconsistencies in tracking data lineage across the AI lifecycle. The Data Platform Lead identifies that a crucial architectural control was missed, affecting transparency and auditability. As the AI Program Manager, you must help ensure that appropriate controls are in place for future scalability. At which stage of the AI data architecture should the control for traceability and transparency have been established?
A. Where data is first validated and lineage tracking begins
B. Where curated datasets and features are organized for use
C. Where models consume data for training and inference
D. Where enterprise systems originate operational data
Question 4
An AI capability is introduced into a customer service operation with the goal of improving efficiency. Rather than rethinking how work is performed end to end, the existing workflow remains largely untouched, and automation is layered onto a single task late in the process. The lack of holistic process redesign leads to operational friction, user confusion, and only marginal performance gains. Which integration approach describes how the AI was implemented in this scenario?
A. Supervised Autonomy
B. Human-Led Collaboration
C. Transformational Redesign
D. Bolt-on Approach
Question 5
Julianne Moore, Lead AI Systems Architect, is conducting an investigation on a facial recognition access system that recently failed a security audit. The audit team demonstrated that by wearing a specifically crafted pair of noisy pattern eyeglasses, an unauthorized user could consistently trick the system into identifying them as the CEO. Julianne confirms that the system's source code is intact and the original database of face images used to train the model was verified as clean and unaltered. Julianne must categorize this vulnerability in her report to the CISO. Which AI-specific security threat characterizes the method used to bypass the system's identification controls?
A. Prompt Injection
B. Data Poisoning
C. Model Theft
D. Adversarial Attacks
Solutions:
| Question 1 Answer: B | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: D |
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