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Answer: Data ingestion, marking the beginning of the data pipeline by gathering raw data from diverse sources, including extraction and loading into storage systems.
**Correct Answer: D. Data ingestion** Data ingestion is the phase that focuses on the collection and acquisition of data from various sources, which is crucial for the initial setup of the data pipeline in a machine learning project. It involves data extraction from sources like databases, files, APIs, and loading the data into storage systems such as data warehouses or lakes. This phase is foundational for ensuring that the subsequent steps of data pre-processing, transformation, and model training have access to the necessary raw data. **Incorrect Options**: - **A. Data pre-processing**: While important for cleaning and preparing data for analysis, this phase occurs after data has been ingested. - **B. Data transformation**: This is a specific step within data pre-processing where data is reformatted or normalized for analysis, not the initial collection. - **C. Model deployment**: This phase involves putting a trained model into production and is unrelated to the initial data collection and acquisition.
Author: LeetQuiz Editorial Team
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In the development of a machine learning project aimed at predicting customer churn for a telecommunications company, the team is in the initial stages of setting up their data pipeline. The project requires the integration of data from multiple sources including CRM systems, call detail records, and customer feedback forms. The team is discussing the phases of data preparation and processing. Considering the need for scalability, cost-efficiency, and compliance with data privacy regulations, which phase is primarily responsible for the collection and acquisition of data from these diverse sources? Choose one correct option.
A
Data pre-processing, as it involves cleaning and preparing the data for analysis, ensuring compliance with data privacy regulations.
B
Data transformation, where data is reformatted and normalized for analysis, crucial for handling the diversity of data sources.
C
Model deployment, the phase where the trained model is put into production, unrelated to the initial data collection.
D
Data ingestion, marking the beginning of the data pipeline by gathering raw data from diverse sources, including extraction and loading into storage systems.
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