AML Project Planning & Execution 2

Question 1

During the scoping phase of an ML project, a team discovers the labeled dataset has only 500 samples for a 10-class classification task. What is the MOST appropriate first response?

Accepted Answer

Re-evaluate feasibility and explore data augmentation or semi-supervised approaches

Answer

Re-evaluating feasibility and exploring augmentation or semi-supervised learning addresses the core data scarcity problem before committing to a training approach.

Question 2

A stakeholder requests a real-time fraud detection system with sub-10ms latency. Which planning consideration is MOST critical to address early?

Accepted Answer

Defining the latency budget across all pipeline components including inference, pre-processing, and I/O

Answer

End-to-end latency budgeting must account for all pipeline stages, not just model inference, and must be validated early to avoid architectural rework.

Question 3

Which artifact BEST serves as the single source of truth for tracking ML experiment reproducibility across a team?

Accepted Answer

An MLflow or similar experiment tracking registry logging code version, data hash, params, and metrics

Answer

Experiment tracking platforms like MLflow capture code version, data lineage, hyperparameters, and metrics in a queryable, reproducible format.

Question 4

A project manager wants to estimate compute costs for training a large language model. Which factor has the GREATEST impact on total cost?

Accepted Answer

Model parameter count, dataset size, and number of training steps combined

Answer

Compute cost scales with the product of model size, data volume, and training steps, making these the dominant cost drivers.

Question 5

When defining the success criteria for an ML project, what is the PRIMARY risk of using accuracy as the sole metric for an imbalanced dataset?

Accepted Answer

A model predicting only the majority class can achieve high accuracy while failing on the minority class

Answer

On imbalanced datasets, a trivial majority-class predictor can achieve misleadingly high accuracy, masking complete failure on the minority class.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Project Planning & Execution 2