Hey everyone, Iām currently on my journey to prepare for the Databricks Certified Data Engineer Professional exam and have been exploring multiple resources, including Databricks documentation, hands-on labs, and practice exercises. Midway through my prep, I started using Certs Matrix, which has been really helpful in practicing scenario-based questions and connecting theoretical knowledge to real-world data engineering challenges. Hereās a scenario Iām trying to clarify Suppose you are managing a Delta Lake pipeline that ingests semi-structured streaming data from multiple sources. To ensure both high performance and consistent data quality, should you focus on schema evolution, partitioning strategies, or caching intermediate results? Iād greatly appreciate insights from anyone who has taken the exam or faced similar real-world pipelines. Your guidance would help me validate my approach and continue preparing confidently. Thanks in advance!
