Extract Load and Transform (ELT) is correct because it describes loading large volumes of raw records into storage first and then transforming them inside the data warehouse or analytics store, which matches workflows that build advanced models from many stored records and that usually run as scheduled batch jobs.

ELT is well suited to model training and analytics because it allows raw data to be persisted where scalable query engines and SQL based transformations can operate directly on the stored records, and those transformations and feature engineering steps are commonly implemented as scheduled batch jobs.

Extract, Transform, and Load (ETL) is incorrect because it performs transformations before loading into the target store, and that pattern is less common for modern analytics workflows that rely on loading raw data first and transforming inside the warehouse for large scale model building.

Cloud Dataflow is incorrect because it is a specific Google Cloud service for stream and batch data processing and not the general workflow term that describes loading many records into storage and then transforming them for model building.

Atomicity Consistency Isolation and Durability (ACID) is incorrect because it refers to transactional properties of databases and has nothing to do with the described batch model training workflow.

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