Google Exam Professional-Machine-Learning-Engineer Topic 8 Question 57 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 57
Topic #: 8

[All Google Professional Machine Learning Engineer Questions]

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

AUse the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.

BExport data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.

CConvert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.

DUse TensorFlow I/O's BigQuery Reader to directly read the data.

Show Suggested Answer

Suggested Answer: B

by Jennie at Dec 24, 2022, 10:44 AM

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