Answer-first summary for fast verification
Answer: The target is used to store the processed data securely, complying with financial regulations, and the notebook libraries contain the scalable code and logic for processing the data, enabling dynamic workload scaling.
Option C is the correct answer because it accurately describes the primary purposes of the target and notebook libraries in a DLT pipeline under the given constraints. The target securely stores processed data in compliance with financial regulations, while the notebook libraries contain the code and logic that enable dynamic scaling of the pipeline to meet workload demands efficiently. Option A is incorrect because the target stores processed, not raw, data. Option B is incorrect because the primary purpose of notebook libraries is not visualization but processing logic. Option D is incorrect because it reverses the roles of the target and notebook libraries.
Author: LeetQuiz Editorial Team
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In the context of designing a Data Lake and Analytics (DLT) pipeline on Azure Databricks for a financial services company, you are tasked with ensuring the pipeline meets strict compliance, scalability, and cost-efficiency requirements. The pipeline must process sensitive financial data, scale dynamically with workload, and minimize operational costs. Considering these constraints, what is the primary purpose of the target and the notebook libraries in the pipeline? Choose the best option from the following:
A
The target is used to store the raw data, and the notebook libraries contain the code and logic for processing the data, ensuring compliance with data governance policies.
B
The target is used to store the processed data in a cost-efficient manner, and the notebook libraries are used to visualize the data for business insights.
C
The target is used to store the processed data securely, complying with financial regulations, and the notebook libraries contain the scalable code and logic for processing the data, enabling dynamic workload scaling.
D
The target is used to manage the data workflow efficiently, and the notebook libraries are used to store the processed data in a scalable and cost-efficient manner.