Data Virtualization, Lakehouse, Platform and Architecture Quiz

User requirement in a data warehouse project is urgent but not yet confirmed, may change in a short term. Which way is recommended?

• Prototype with a view to deliver solution, and change the view logic along with the requirement change, and rebuild it to ETL after requirement is confirmed.

• Directly develop ETL process base on developer's understanding because it saves effort.

• Develop ETL process after user requirement is finally confirmed.

Which of the below belongs to Data Virtualization? (multiple choice)

• Pivot table of raw data table in an Excel worksheet.

• Physical table stored in Storage Grid.

• A view built on a DB table.

• a data service API, the data is from heterogeneous data sources.

Which of the following is NOT the capacity of a Data Virtualization Platform?

• ETL

• Caching

• Optimization

• Transformation

Which of the following is in Operation phase of a Data Virtualization project?

• User acceptance test

• Design Metadata

• Monitor performance

• Study and explore source data

what is a data lakehouse？ (multiple choice)

• A data lakehouse removes the walls between lakes and warehouses

• marrying the low-cost, flexible storage of a data lake with the data management, schema, and governance of a warehouse

• A data lakehouse acts as a single platform for storing both structured and unstructured data, supporting a variety of workloads from batch processing to real-time analytics.

• Data lakehouses enforce schema on read and write operations, bringing the governance and reliability of data warehouses to the flexibility of data lakes.

What is the main benefit of using a data lakehouse architecture over traditional data warehouses?

• Reduced data storage capabilities

• Improved performance for transactional databases

• Enhanced data governance and a unified platform for different data workloads

• Higher dependency on proprietary file formats

Which feature of a data lakehouse ensures transactional consistency and accuracy for data analytics workloads?

• Use of structured data only

• ACID transactions

• Dependence on a single data format

• Lack of schema enforcement

Which of the following is NOT included in Cloud Success?

• Orchestration

• Well-developed UI/UX

• Infrastructure as Code

• Automatically Scale-up and Scale-down

Select all features of Commoditized AI. (multiple choice)

• Business drive machine learning

• Data become business centric

• Commoditized AI help developer to develop and debug

• Embrace customer 360 with machine learning and deep learning

Which of the following are KubeFlow conceptual layer? (multiple choice)

• ML Tools

• KubeFlow Applications and scaffolding

• Platform/Layer

• Code