You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the…

Option A is correct because importing the TensorFlow model with BigQuery ML, and running the

ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a

custom TensorFlow model, and store the predicted results in another BigQuery table. BigQuery ML

allows you to import TensorFlow models that are stored in Cloud Storage, and use them for

prediction with SQL queries1. The ml.predict function returns a table with the predicted values,

which can be saved to another BigQuery table2.

Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the

BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline

than option A. The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow

datasets, which can be used for training or prediction3. However, this option also requires writing

code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results

back to BigQuery4.

Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to

TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery

requires more effort to build the inference pipeline than option A. Dataflow is a service for creating

and running data processing pipelines, such as ETL (extract, transform, load) or batch processing5.

Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction.

However, this option also requires writing code to create the Dataflow pipeline, convert the data to

TFRecords, run the batch inference, and write the results to BigQuery.

Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the

BigQuery I/O connector with a custom function to perform the inference within the pipeline, and

writing the results to BigQuery requires more effort to build the inference pipeline than option A.

The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow

pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create

the custom function for inference, and write the results to BigQuery.

Reference:

Importing models into BigQuery ML

Using imported models for prediction

TensorFlow BigQuery reader

BigQuery API

Dataflow overview

[Vertex AI Prediction overview]

[Batch prediction with Dataflow]

[BigQuery I/O connector]

[Using TensorFlow models in Dataflow]