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

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?
B) Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
A) Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
C) Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
D) Use TensorFlow I/O's BigQuery Reader to directly read the data.

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

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 57
Topic #: 8
[All 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?

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Suggested Answer: B

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

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Pansy
10 months ago
Not sure about that, exporting to CSV seems safer to me.
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Ronna
10 months ago
I think option D is the best choice for scalability.
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Martina
10 months ago
Wait, can TensorFlow I/O really handle that much data directly?
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Azalee
11 months ago
Definitely going with option C! Makes the most sense.
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Hermila
11 months ago
I heard using TFRecords is super efficient for large datasets.
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Val
11 months ago
Using TensorFlow I/O's BigQuery Reader sounds familiar, and I think it could help avoid bottlenecks, but I need to double-check its scalability.
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Shay
11 months ago
I feel like converting data to TFRecords could be beneficial for performance, but I can't recall the exact advantages over other methods.
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Reita
11 months ago
I think exporting to CSV files is a common approach, but it might introduce some latency during data loading.
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Frederica
11 months ago
I remember we discussed using the BigQuery client library, but I'm not sure if that's the most efficient way for large datasets.
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Margot
11 months ago
I'm feeling pretty confident about this one. The scenarios describe different ways the host can be configured to connect to the two arrays, so I just need to analyze each option carefully.
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Arminda
11 months ago
I vaguely remember something about digests being generated from the content. So, if the content is identical but has more whitespace, would that really affect the digest?
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