Where the last two attempts failed, by ECO task

May 9, 2026: fail. Sept 6, 2026: fail. One retake left. No date until every task on this board is clean.

5 of 37: Low on both attempts 11 of 37: Low in September 4 of 37: Not tested in September 17 of 37: Mid or High in September 37 ECO tasks

Where the September Lows sit, weighted by exam share

Low tasks ÷ domain tasks × weight
  • Business Needs 26% 6 of 10 Low
  • Operationalize 17% 4 of 7 Low
  • Model Dev & Eval 16% 3 of 6 Low
  • Data Needs 26% 2 of 9 Low
  • Trustworthy AI 15% 1 of 5 Low
Domain 37 shown

Low on both attempts (5)

Task May → Sept Study Hall Logged
II.7 Business Needs Draft AI solution Low → Low 3 –
V.3 Operationalize Oversee model governance Low → Low 1 –
II.6 Business Needs Manage adoption/integration risks Low → Low – –
V.7 Operationalize Oversee AI solution contingency plan Low → Low – –
IV.1 Model Dev & Eval Oversee AI/ML model technique(s) (e.g., algorithm, selection) Low → Low – –

Low in September (11)

Task May → Sept Study Hall Logged
I.5 Trustworthy AI Manage accountability documentation and audit trail High → Low 3 –
III.9 Data Needs Convey data understanding to leadership Mid → Low 2 –
IV.3 Model Dev & Eval Manage AI/ML model training Mid → Low 2 –
II.1 Business Needs Identify problem to be solved (e.g., needs, persona) Mid → Low 1 –
III.4 Data Needs Coordinate AI workspace and infrastructure High → Low 1 –
IV.5 Model Dev & Eval Verify data quality for go/no-go decision to conduct data preparation Mid → Low 1 –
II.2 Business Needs Evaluate initial AI feasibility High → Low – –
II.5 Business Needs Determine ROI High → Low – –
II.10 Business Needs Identify project resources (e.g., people, hardware, contractors) Mid → Low – –
V.2 Operationalize Manage AI solution deployment Mid → Low – –
V.6 Operationalize Manage AI solution transition plan not tested → Low 1 –

Not tested in September (4)

Task May → Sept Study Hall Logged
II.4 Business Needs Develop AI project scope statement Low → not tested 1 –
III.7 Data Needs Oversee data evaluation Low → not tested 1 –
V.5 Operationalize Prepare final report/lessons learned Low → not tested – –
II.9 Business Needs Support business case creation High → not tested – –

Mid or High in September (17)

Task May → Sept Study Hall Logged
III.6 Data Needs Check data privacy, compliance, and access High → Mid 1 –
III.8 Data Needs Determine if data meets solution needs High → Mid 1 –
IV.4 Model Dev & Eval Manage data transformation to conduct data preparation High → High 3 –
III.1 Data Needs Define required data Mid → Mid 1 –
V.4 Operationalize Oversee AI solution metrics (e.g., KPI, model performance) Mid → High 1 –
IV.2 Model Dev & Eval Oversee AI/ML model QA/QC (e.g., configuration management, model performance) not tested → High 1 –
IV.6 Model Dev & Eval Verify model ready for operationalization go/no-go decision Mid → High 1 –
I.2 Trustworthy AI Manage AI/ML transparency (e.g., data selection, algorithm selection) High → High 1 –
I.3 Trustworthy AI Conduct bias checks (e.g., model, data, algorithm) Low → High 1 –
II.3 Business Needs Conduct risk assessment(s) (e.g., security, safety, ethics) Low → Mid – –
II.8 Business Needs Define success criteria (e.g., KPIs, metrics) Mid → Mid – –
III.2 Data Needs Identify data SMEs Low → Mid – –
III.3 Data Needs Identify data sources and locations Low → Mid – –
III.5 Data Needs Gather required data Low → High – –
V.1 Operationalize Manage creation of AI solution deployment plan High → High – –
I.1 Trustworthy AI Oversee privacy and security plan High → High – –
I.4 Trustworthy AI Monitor regulatory and policy compliance Low → Mid – –
Log a miss Guides: Trustworthy AI (I)Data Needs (III)Model Dev & Eval (IV)Operationalize (V) Glossary