Domain II · Identify Business Needs and Solutions · 26% of the exam

II.7 Draft AI solution

Task 7 of Identify Business Needs and Solutions

May 9: Low Sept 6: Low Study Hall misses: 3 Logged: 0

Enablers (verbatim, ECO)

PMI, ECO p. 5: enablers are "illustrative examples of the work associated with the task" and "not meant to be an exhaustive list but rather offer a few examples to help demonstrate what the task encompasses."

Ours: PMI's stems paraphrase one of these as a symptom; the right option is that enabler in PMI's words; the distractors are its siblings or a neighbouring task's enablers.

Create high-level architecture for AI system design
Define data flow and processing requirements
Specify AI model types and algorithmic approaches
Document integration points with existing systems
Outline deployment and operational considerations

The rule

Draft at high level across all five enablers. When asked for a model type, map the problem shape: predefined classes mean classification, unlabeled grouping means clustering, a continuous value means regression, adapting over time means reinforcement learning, images mean recognition. Requirements validated means draft the concept next, not another validation.

Study Hall misses on this task (3)

Practice II #9 · confidence Medium · 54s

Scenario: Scheduling optimisation that must keep adapting to changing conditions; which model type to propose

Decided by: Continuous adaptation and sequential decisions point to reinforcement learning

Chose: An image-based model type
Full-length Q56 · confidence High · 20s

Scenario: Assign customers to existing, predefined segments; which model type

Decided by: Predefined classes mean classification; clustering is for unlabeled grouping

Chose: Clustering
Full-length Q24 · confidence Medium · 44s

Scenario: Requirements gathered and success criteria already validated; NEXT

Decided by: One step forward: draft the solution concept

Chose: Another stakeholder validation meeting (stepping back)

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What to read

Workbook p. 22-23 — AI Pattern Identification
Module 01 Lesson 13: A Guide to Project Fit Using the Seven Patterns; Lesson 14: A Business-Centric Guide to the Seven Patterns of AI
Module 04 Lessons 3-4: Machine Learning Algorithm and Model; Machine Learning Algorithm Basics; Lesson 6: Other Models
Leading and Managing AI Projects p. 19 — Mapping Business Needs to AI Patterns: Translating Requirements Into Technical Strategy