Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a…

Question

Options

• A
  1. Create one lake for each team. Inside each lake, create one zone for each domain.
• B
  1. Create one lake for each team. Inside each lake, create one zone for each domain.
• C
  1. Create one lake for each domain. Inside each lake, create one zone for each team.
• D
  1. Create one lake for each domain. Inside each lake, create one zone for each team.

• A
  5% (1)
• B
  5% (1)
• C
  75% (15)
• D
  15% (3)

Explanation

Option C correctly applies data mesh principles by mapping Dataplex lakes to domains (airlines, hotels, ride-hailing) and zones to teams (analytics, data science) within each domain. This structure gives each domain ownership of its data assets - domain teams can independently manage pipelines and assets without routing everything through a central team, directly eliminating the bottleneck described.

Options A and B are wrong because organizing lakes by team rather than domain inverts the data mesh model: data mesh is fundamentally domain-centric, not team-centric. Structuring around teams re-centralizes ownership and doesn't give domains the autonomy needed to move independently.

Options C and D share the same first step, but D likely differs in subsequent steps (e.g., how Data Catalog, IAM, or asset registration is configured) in ways that break proper domain isolation or governance - the distinction is in the details of how ownership and access are wired up.

Memory tip: Think of a lake as a country (the domain owns it) and zones as cities inside (where each team does its work). Data mesh = domain sovereignty, so the domain always gets the top-level container.

No community discussion yet for this question.