Build and Automate Pipelines CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is a core principle of data mesh?

   • AStoring all data in a single monolithic warehouse
   • BCentralized data governance by a single team
   • CDomain-oriented decentralized data ownership
   • DEliminating the need for data pipelines
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A small marketing agency with limited engineering resources needs to automate a workflow that extracts leads from a web form, sends them to a CRM, and posts a notification to a Slack channel. The team has no experience with complex coding or infrastructure management. Which type of tool is most appropriate for this task?

   • AA data virtualization platform.
   • BA low-code automation platform like Zapier or Microsoft Power Automate.
   • CA custom Python application deployed on a cloud VM.
   • DAn orchestration tool like Apache Airflow.
   Reveal Solution Discussion Select an answer first
4. 13application · medium

   A marketing analytics team needs to load data from a CRM system into their cloud data warehouse. The data must be cleansed (e.g., deduplicated, standardized formats) before loading. The team wants to use a modern, code-based approach where the transformation logic is transparent and version-controlled. Which tool is best suited for this task?

   • AA traditional ETL tool with a graphical drag-and-drop interface.
   • Bdbt (data build tool) to define transformations as SQL SELECT statements.
   • CFivetran to handle the transformation and loading.
   • DA custom Python script that runs on a schedule.
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5. 14application · medium

   A global bank wants to provide its risk analysts with a single, consistent view of customer data that is stored in multiple regional databases and a cloud data warehouse. The bank does not want to physically consolidate all data into one location due to data residency regulations. They need an architecture that can query across these distributed sources as if they were one. Which architecture is most suitable?

   • AData fabric, which uses metadata and virtualization to provide unified access across distributed environments.
   • BA data mesh, where each region owns its data and shares it via APIs.
   • CA single, centralized data lake in one region.
   • DA point-to-point integration between each regional database and the analysts' tools.
   Reveal Solution Discussion Select an answer first
6. 15application · medium

   A software company is redesigning its data pipeline. They want to break down a monolithic ETL application into smaller, independently deployable components that communicate over HTTP. The goal is to make the pipeline more resilient and easier to scale individual parts. Which architectural style best supports this requirement?

   • AA service-oriented architecture using microservices and APIs.
   • BA data mesh approach where each domain owns its data.
   • CA single, large batch job that runs all steps in sequence.
   • DA data virtualization layer that abstracts the underlying ETL logic.
   Reveal Solution Discussion Select an answer first