Faith and Jerrie from dbt Labs provide guidance on where to place tests within a data pipeline to ensure data quality and reliability. They suggest structuring tests according to different layers of the pipeline: source, staging, intermediate, and marts layers. Source tests focus on issues fixable at the source system, while staging tests address business-focused anomalies and data cleanup. Intermediate tests emphasize data hygiene and anomaly detection in new columns, and marts tests target net-new columns with complex transformation logic. The authors also discuss the importance of utilizing CI/CD for automating these tests, and they introduce advanced CI features in dbt Cloud to streamline changes and peer reviews. They highlight the iterative nature of testing strategies, encouraging readers to adapt and refine their approaches over time while providing feedback to improve the shared guidance.