A logistics company needs to provide real-time shipment tracking information to its customer portal. The data is currently stored in a legacy on-premises database and a cloud-based order management system. The company wants to avoid building and maintaining a complex ETL pipeline to copy data into a new database. What is the most efficient way to provide this real-time data access?

Use a data lake to store snapshots of the data and query it with Spark.

Use data virtualization to create a virtual view that queries both systems in real-time.

Create a microservice that extracts data from both systems and exposes it via an API.

Build a nightly batch ETL job to copy data from both systems into a new reporting database.

A multinational corporation is defining its enterprise data strategy. The strategy must ensure that all business units can access a single, consistent view of customer data for analytics, while also allowing individual business units to maintain control over their own data quality. The data architecture team is tasked with designing the pipeline integration. Which architectural approach aligns best with this enterprise data strategy?

A point-to-point integration between every business unit's database and the central analytics team.

A data mesh architecture where each business unit owns and operates its data products, with a centralized governance layer for global standards.

A centralized data warehouse where all business units must load their data into a single, shared schema.

A single, monolithic data lake where all raw data is dumped without any schema or ownership.

A global manufacturing company has data in multiple on-premises plant databases, a cloud ERP, and various SaaS tools. They want to implement a data fabric to provide a unified view. However, they have a strict budget and cannot afford to replace their existing systems. They also have latency-sensitive operational use cases that need real-time data access. Which implementation approach is most appropriate?

Implement a data fabric that uses data virtualization to connect to existing systems, providing a logical, real-time view without physical data movement.

Implement a data mesh and have each plant manage its own data.

Build a new centralized data warehouse and migrate all data into it.

Create a data lake and copy all data into it for analysis.

CertNexusCertified Data Science Practitioner (CDSP)

Domain 6Objective 1

Build and Automate Pipelines CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Part of the 6.0 Operationalizing the pipeline domain, which accounts for 5-8% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

39questions here

8free pages

11concepts

5-8%of the exam

Questions 16–20

16foundation · easy
Which component of an enterprise data strategy would most directly affect how data pipelines handle sensitive customer information?
Select an answer first
17application · medium
A logistics company needs to provide real-time shipment tracking information to its customer portal. The data is currently stored in a legacy on-premises database and a cloud-based order management system. The company wants to avoid building and maintaining a complex ETL pipeline to copy data into a new database. What is the most efficient way to provide this real-time data access?
Select an answer first
18foundation · easy
Which tool is primarily used to automate the extraction and loading of data from source systems into a central repository, reducing the need for custom ingestion code?
Select an answer first
19application · medium
A multinational corporation is defining its enterprise data strategy. The strategy must ensure that all business units can access a single, consistent view of customer data for analytics, while also allowing individual business units to maintain control over their own data quality. The data architecture team is tasked with designing the pipeline integration. Which architectural approach aligns best with this enterprise data strategy?
Select an answer first
20expert · hard
A global manufacturing company has data in multiple on-premises plant databases, a cloud ERP, and various SaaS tools. They want to implement a data fabric to provide a unified view. However, they have a strict budget and cannot afford to replace their existing systems. They also have latency-sensitive operational use cases that need real-time data access. Which implementation approach is most appropriate?
Select an answer first

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Build and Automate Pipelines CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Questions 16–20

Which component of an enterprise data strategy would most directly affect how data pipelines handle sensitive customer information?

Which tool is primarily used to automate the extraction and loading of data from source systems into a central repository, reducing the need for custom ingestion code?

Keep studying — 6.0 Operationalizing the pipeline