CT-AI Certified Tester AI Testing

Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:

"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

The ISTQB CT-AI syllabus describes the ML data preparation workflow in Section 2.2 – Data Preparation . Data preparation consists of data gathering , cleaning , transformation , and sampling . The syllabus emphasizes that one significant challenge during data gathering is combining data from multiple heterogeneous sources , which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C .

Option A is incorrect because erroneous data correction is part of cleaning , not transformation. Option B contradicts the syllabus: while automation can help, not all steps should be automated due to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk—particularly around representativeness—and the syllabus emphasizes caution, not complacency.

Thus, Option C is the only statement that accurately reflects the syllabus.