Google Professional Machine Learning Engineer
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Your team is engaged in numerous ML projects for an international consulting firm that requires handling large datasets across different cloud environments. Management has opted to store the majority of data for ML models in BigQuery, citing its ease of preprocessing and transformations with standard SQL, along with its structured nature offering efficiency, integration, and security. Given the need to develop and adjust code for direct BigQuery data access across various modeling environments, including TensorFlow and Dataflow, and considering the constraints of multi-cloud accessibility and the need for efficient data processing, which tools would you utilize? (Select 3 options)
Explanation:
The BigQuery Python client library is essential for accessing BigQuery data across various frameworks. The BigQuery I/O Connector is crucial for direct BigQuery connections from Dataflow, enabling efficient data processing. The tf.data.dataset reader is specifically used for connecting directly to BigQuery from TensorFlow or Keras, facilitating model training with BigQuery data. BigQuery Omni, while useful for multi-cloud analytics, does not directly facilitate code development for accessing BigQuery data in modeling environments. Apache Beam with BigQuery I/O is another valid option for processing BigQuery data in Dataflow pipelines, but it was not included in the original correct options to maintain consistency with the original question's answers.