
Ultimate access to all questions.
As a Google Professional Machine Learning Engineer at a startup utilizing Google Cloud, you are tasked with optimizing the data pipeline for multiple TensorFlow models. The data is in Parquet format, and the team requires a solution that efficiently manages this data both as input and output, without incurring additional infrastructure costs or complexity. The solution must also ensure seamless integration with TensorFlow to maintain development velocity. Considering these constraints, which of the following solutions should you implement? (Choose one correct option)
A
Implement Dataflow Flex Templates to process and convert Parquet files into a format compatible with TensorFlow, despite the additional transformation steps and potential cost implications.
B
Utilize BigQuery to export data into TFRecords format, leveraging BigQuery's scalability but introducing additional data transformation steps and costs.
C
Integrate TensorFlow I/O to directly handle Parquet files within TensorFlow, avoiding unnecessary data transformations and additional costs.
D
Deploy Cloud Dataproc to process Parquet files, accepting the additional infrastructure management and costs for compatibility with TensorFlow.