Build and Automate Pipelines CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   Which element is typically part of a data management architecture that facilitates pipeline integration?

   • AMarketing automation software
   • BOffice productivity suites
   • CData catalogs and metadata management
   • DEmployee training programs
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2. 27foundation · easy

   What is the primary role of an API in a micro-services-based data pipeline?

   • ATo replace the need for data transformation
   • BTo provide a standardized interface for services to communicate
   • CTo ensure data is always encrypted
   • DTo store the data for the pipeline
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4. 28expert · hard

   A company is considering using data virtualization to provide a real-time view of data from its operational systems for reporting. However, the reporting team is concerned about query performance, as some source systems are slow and the reports are complex. The team is weighing data virtualization against a traditional ETL approach. What is the most important trade-off to consider?

   • AData virtualization is only suitable for batch reporting, not real-time access.
   • BData virtualization is always faster than ETL because it avoids data movement.
   • CData virtualization provides real-time data but may have lower query performance on complex joins across sources, whereas ETL provides faster query performance but with data latency.
   • DETL is always more cost-effective than data virtualization because it uses cheaper storage.
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5. 29application · medium

   A large e-commerce company is experiencing bottlenecks because a central data team is responsible for all data pipelines for different domains like sales, inventory, and customer support. The company wants to scale by making each domain team responsible for its own data. Which architectural principle should they adopt?

   • AData virtualization, to provide a single query interface to all domain data.
   • BA centralized data warehouse, to enforce a single source of truth.
   • CData fabric, to create a unified data management layer across all domains.
   • DData mesh, where each domain team owns and operates its data pipelines and products.
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6. 30expert · hard

   A fintech startup has a data pipeline built as a single monolithic application. As the company grows, they face issues: a bug in the data validation step causes the entire pipeline to fail, and they cannot scale the transformation step independently from the loading step. They decide to refactor into microservices. What is the most critical architectural consideration to ensure the refactored pipeline is resilient and maintainable?

   • AEnsuring each microservice has its own database to avoid coupling.
   • BCreating a single, large API gateway to handle all inter-service communication.
   • CEnsuring all microservices are deployed on the same server to reduce latency.
   • DUsing a shared database for all microservices to ensure data consistency.
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