Apache Parquet is the correct choice for maximizing processing efficiency in Azure Machine Learning.

Apache Parquet is a columnar file format and it reduces I O by reading only the columns that are needed instead of whole rows. It supports efficient compression and encoding schemes which lowers storage size and speeds up data transfer which is important for files larger than 3 GB. The format is also splittable which enables parallel reads by distributed compute engines and that improves throughput for large scale machine learning workflows.

TFRecords is optimized for TensorFlow sequential record consumption and it is not a columnar, schema rich format. It can be efficient for TensorFlow training pipelines but it does not provide the same column pruning and wide ecosystem support for analytics engines as Parquet.

XLSX is a spreadsheet format that is not designed for large scale distributed processing. It has significant parsing overhead and it is not splittable which makes it unsuitable for multi node reads of multi gigabyte files.

CSV is a simple row based text format and it lacks an explicit schema and efficient columnar storage. CSV files often require more I O to scan and more CPU to parse which slows distributed processing compared with a compressed, columnar format like Parquet.

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