Google Professional Machine Learning Engineer
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You are tasked with optimizing the input/output performance of a TensorFlow model that processes a structured dataset comprising 100 billion records distributed across multiple CSV files. The solution must consider cost-effectiveness, scalability, and compatibility with TensorFlow's ecosystem. Which of the following strategies would you recommend? (Choose one.)
A
Transform the CSV files into shards of TFRecords and utilize Cloud Storage for data storage, leveraging TensorFlow's native support for TFRecords and Cloud Storage's scalability.
B
Load the data into BigQuery and access the data directly from BigQuery, taking advantage of its serverless architecture and SQL interface for data manipulation.
C
Convert the CSV files into shards of TFRecords and store them in the Hadoop Distributed File System (HDFS), utilizing HDFS's distributed storage capabilities.
D
Import the data into Cloud Bigtable and read the data from Cloud Bigtable, benefiting from its low-latency and high-throughput capabilities for large-scale datasets.