AML Project Planning & Execution 3

Question 1

Which risk is MOST associated with skipping a formal problem framing document before starting ML development?

Accepted Answer

Misalignment between the business objective and the ML task formulation, leading to a technically correct but business-irrelevant model

Answer

Without formal problem framing, teams often optimize a proxy metric that doesn't map to the true business goal, producing a model that performs well on paper but delivers no value.

Question 2

What is the purpose of a 'model card' in ML project execution?

Accepted Answer

A structured document describing a model's intended use, performance across subgroups, limitations, and ethical considerations

Answer

Model cards are standardized documentation artifacts that communicate a model's capabilities, limitations, and responsible use guidelines to downstream stakeholders.

Question 3

During ML project planning, 'technical debt' most commonly accumulates when:

Accepted Answer

Shortcuts like hard-coded thresholds, undocumented preprocessing steps, and monolithic notebooks are used to hit short-term deadlines

Answer

Hard-coded values, undocumented transformations, and notebook-first development create fragile, untestable systems that are costly to maintain and extend.

Question 4

A company wants to deploy an ML model under a strict regulatory compliance requirement. Which planning step is UNIQUELY critical compared to a non-regulated deployment?

Accepted Answer

Documenting model lineage, maintaining audit trails, and planning for explainability requirements before development begins

Answer

Regulated environments require audit trails, explainability artifacts, and lineage documentation that must be architected into the system from the start, not added retroactively.

Question 5

When planning compute resources for distributed ML training, what is 'communication overhead' and why does it matter?

Accepted Answer

The time gradient synchronization across workers takes, which can dominate wall-clock training time when the model-to-data ratio is high

Answer

In distributed training, gradient synchronization overhead can exceed compute time per step, especially for large models on slow interconnects, making network topology a critical planning factor.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Project Planning & Execution 3