Exam Professional Data Engineer topic 1 question 210 discussion

just like MaxNRG said

Answer D

D. 1. Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product. Lake: A logical construct representing a data domain or business unit. For example, to organize data based on group usage, you can set up a lake for each department (for example, Retail, Sales, Finance). Zone: A subdomain within a lake, which is useful to categorize data by the following: Stage: For example, landing, raw, curated data analytics, and curated data science.

D: 1 virtual lake per Data Product (which stands for domain basically), zones to split data by "status". Each Data Eng team can access their own data exclusively and in a data mesh compliant way

The best approach is to create a Dataplex virtual lake for each data product, with multiple zones for landing, raw, and curated data. Then provide the data engineering teams with access only to the zones they need within the virtual lake assigned to their product. To enable teams to easily share curated data products, you should use cross-lake sharing in Dataplex. This allows curated zones to be shared across virtual lakes while maintaining data isolation for other zones.

I believe the answer is B, but there is a misspelling in the answer, should say "create multiple zones"

Each lake should be created per data product since data product sounds like a domain in this question. Since we have landing, raw, curated data, we should create different zones. "Zones are of two types: raw and curated. Raw zone: Contains data that is in its raw format and not subject to strict type-checking. Curated zone: Contains data that is cleaned, formatted, and ready for analytics. The data is columnar, Hive-partitioned, and stored in Parquet, Avro, Orc files, or BigQuery tables. Data undergoes type-checking- for example, to prohibit the use of CSV files because they don't perform as well for SQL access." Ref: https://cloud.google.com/dataplex/docs/introduction#terminology

why not B?

Why not B?

The answer is D

Virtual Lake per Data Product: Each virtual lake acts as a self-contained domain for a specific data product, aligning with the data mesh principle of decentralized ownership and responsibility. Team Autonomy: Teams have full control over their virtual lake, enabling independent development, management, and sharing of their data products.