Large Language Models (LLMs) often underperform in domain adaptation for industrial settings, where available corpora are limited and structurally diverse. These corpora frequently include non-natural formats such as tables, entity lists, or bullet-point instructions that hinder effective learning. To understand and improve domain-adaptive pretraining on such data, we introduce SParK-Eval (Structure-aware Parametric Knowledge Evaluation), a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure (e.g., natural sentence, table, list). This enables fine-grained analysis of how input structure affects parametric knowledge acquisition during DAPT. Additionally, we propose a prompt-based input normalization method that converts diverse inputs into coherent natural sentences, providing a reference for isolating structural effects. Our experiments show that LLMs acquire substantially more knowledge from natural sentences than from their structurally non-standard counterparts. These findings underscore the importance of structure-aware evaluation in diagnosing learning challenges and guiding effective domain adaptation strategies.