Free Databricks Certified Data Engineer Associate Actual Exam Questions - Question 8 Discussion
data engineering team’s reports. The leader believes the siloed nature of their organization’s data
engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
B/C? Having one source of truth (B) definitely helps, but if teams reorganize under one department (C), that might improve communication and reduce silos too. Still, B feels more direct for the data consistency issue.
I agree that having a single source of truth (B) is crucial here. Without consistent data inputs, autoscaling (A) or faster responses (E) won’t fix the core mismatch in reports. Could the problem really be solved without unified data access?
Option D makes sense too because if both teams can work together in real-time on the same platform, it cuts down miscommunication and version mismatches. Sure, the core problem is data consistency, but real-time collaboration can prevent discrepancies from even happening by aligning everyone’s work as it’s done. So, while B addresses the root cause directly, D helps by improving the process around it, which also leads to more consistent reports.
B/C? The single source of truth (B) definitely helps, but reorganizing teams (C) could also reduce silos and improve alignment, though it’s less about the tech and more about structure.
B/E? Having one source of truth definitely helps with consistent reports, but being able to respond faster to requests (E) is an added benefit from that unified setup. Still, B hits the core problem better.
B/D? I get why B makes sense since a data lakehouse unifies data storage, so both teams pull from the same source. But D also seems plausible because having a shared platform could let them collaborate more directly and maybe even in real-time, cutting down on misunderstandings and delays. Still, B feels more foundational since the core problem is differing reports, which usually means conflicting data sources.
I think the answer is B. A data lakehouse combines data engineering and analysis on a single platform, so both teams use the same source of truth, reducing discrepancies in reports.