Google Professional Machine Learning Engineer
Comments
Loading comments...
Ultimate access to all questions.
Your company has recently migrated its machine learning infrastructure to Google Cloud, transitioning from disk-based block storage to Cloud Storage for file storage and BigQuery for tabular data, with Dataflow handling pre-processing. The team primarily uses TensorFlow and Keras for model development. Given this setup, which of the following methods is NOT suitable for accessing tabular data from BigQuery as part of your machine learning pipeline? Choose the best option.
A
Utilizing the BigQuery I/O Connector within your Dataflow pipeline to read and process tabular data directly from BigQuery.
B
Implementing a custom tf.data.Dataset reader that leverages the BigQuery Storage API to fetch data directly into your TensorFlow model.
C
Using the BigQuery Python client library to query data and load it into a Pandas DataFrame for further processing in your machine learning models.
D
Employing a tf.data.Iterator to directly iterate over BigQuery tables without prior data loading or transformation steps.
E
Combining both the BigQuery Python client library for initial data exploration and the BigQuery I/O Connector for scalable data processing in Dataflow.