The Evolution ofData Products and it’s Ecosystem

Self-check

Questions 1–10 of 10

1. In the data product ecosystem, where does the journey of every data product start?
Question 1A. In the Data Preparation Zone, where raw, fragmented inputs are collected, validated and modelled for reuseB. In the OLAP Zone, where dashboards are builtC. In an external Data-As-a-Service marketplaceD. In the sandbox of a data scientistE. In the CRM system
2. Which modelling approach in the Data Preparation Zone is described as designed for agility, historisation and traceability in complex data landscapes?
Question 2A. 3NF Data Warehouse ModellingB. Data Vault ModellingC. Feature Store DesignD. Dimensional Data MartsE. Semantic Layers
3. What are Ensemble Techniques such as Focal Point and Anchor used for in the Data Preparation Zone?
Question 3A. Training machine learning modelsB. Visualising results in dashboardsC. Unifying fragmented entities across domainsD. Throttling API accessE. Exposing data externally as a service
4. Which of the following is NOT listed as a key component of the OLAP Zone?
Question 4A. Data Marts (dimensional models)B. Semantic LayersC. Inference TablesD. Feature Store DesignE. Sandboxes
5. What do semantic layers in the OLAP Zone do?
Question 5A. Store raw, unvalidated dataB. Enforce throttling on external APIsC. Historise every change to a recordD. Replace the need for data stewardsE. Translate physical schemas into business-friendly views, empowering self-service without SQL knowledge
6. Which of the following is given as an example of a Data-As-a-Product (DAaP) asset?
Question 6A. Curated customer profile datasets used across marketing, support and analyticsB. A one-off spreadsheet emailed to a managerC. A sandbox used for a single experimentD. A raw log file from a web serverE. An unvalidated CSV extract
7. What is Data-As-a-Service (DaaS)?
Question 7A. A modelling technique for data warehousesB. The service delivery model used when DAaP is distributed externally via APIs, flat files or cloud platformsC. An isolated environment for experimentationD. A role responsible for metadata and lineageE. A type of dimensional schema
8. Which of the following is listed as a key trait of Data-As-a-Service (DaaS)?
Question 8A. Unlimited, unthrottled access for everyoneB. No documentation requiredC. SLA-backed reliability and freshnessD. Access only through manual spreadsheetsE. A single internal consumer
9. In the data product ecosystem, which responsibility belongs to the data steward rather than the Data Product Owner?
Question 9A. Defining the DAaP vision and business caseB. Managing backlogs, versions and SLAsC. Leading onboarding and roadmap evolutionD. Governing metadata, lineage and complianceE. Aligning with platform engineering and business units
10. How is the Data Product Owner described within the DAaP ecosystem?
Question 10A. A part-time reviewer of dashboardsB. The database administrator for the warehouseC. An external auditor of data qualityD. The person who writes the ETL codeE. The single-threaded leader responsible for the productisation lifecycle