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Answer: Construct a dataflow that integrates multiple transformation and enrichment techniques, including filtering, joining, aggregations, and windowing, while ensuring scalability, cost efficiency, and compliance with data privacy standards.
Option D is the most comprehensive and balanced approach, addressing the need for deep data analysis while also considering critical factors like cost efficiency, scalability, and compliance with data privacy standards. It combines various techniques to enrich and transform the data, providing a holistic view of customer transactions. Options A, B, and C, while useful in specific contexts, fail to address all the key constraints and requirements of the project, making Option D the best choice for achieving the project's goals.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with enhancing a lakehouse project by creating a dataflow to transform and enrich a large dataset of customer transactions. The dataset includes transaction ID, customer ID, product ID, transaction date, and transaction amount. Your goal is to derive actionable insights while considering cost efficiency, scalability, and compliance with data privacy standards. Which of the following approaches would BEST achieve this goal? (Choose one option.)
A
Implement a dataflow that focuses solely on basic transformations like filtering and sorting, to minimize processing costs.
B
Develop a dataflow that enriches the dataset by joining it with external data sources, such as product details and customer demographics, without considering the impact on scalability.
C
Design a dataflow that performs advanced data processing techniques, such as aggregations and windowing, but overlooks the importance of data privacy compliance.
D
Construct a dataflow that integrates multiple transformation and enrichment techniques, including filtering, joining, aggregations, and windowing, while ensuring scalability, cost efficiency, and compliance with data privacy standards.