Assess learners' ability to evaluate privacy–utility trade-offs in differential privacy deployments for large-scale data analytics systems. Scope includes DP mechanisms (Laplace/Gaussian), epsilon and privacy-budget management, composition and amplification, utility metrics and accuracy loss, system-level constraints (distributed processing, sampling, secure aggregation), tuning noise vs. utility, empirical evaluation/auditing, and identifying implementation pitfalls and fairness impacts; questions should require scenario-based critical analysis and justification.