A data scientist is building a proof of concept for a commercialized machine-learning model.

→ In the proof-of-concept phase, the first practical step is model selection — identifying which modeling technique is most appropriate based on the nature of the problem, data, and business goal. Literature reviews are helpful but usually precede model experimentation.

Why the other options are incorrect:

A: Literature review informs planning but isn't the first hands-on step.

B: Performance evaluation comes after models are built.

C: Hyperparameter tuning applies after a model is chosen.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.1:“Model selection is a critical step during early prototyping when evaluating different algorithms for feasibility.”

CRISP-DM Framework – Modeling Phase:“Selecting candidate models is the first step in model development after understanding the data.”