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Google Professional Machine Learning Engineer Exam - Topic 4 Question 118 Discussion

You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?
C) Import the model into Vertex Al On Vertex Al Pipelines, create a pipeline that uses the DatafIowPythonJobOp and the ModelBatchPredictOp components.
A) Import the model into Vertex Al and deploy it to a Vertex Al endpoint On Vertex Al Pipelines create a pipeline that uses the Dataf lowPythonJobop and the Mcdei3archPredictoc components.
B) Import the model into Vertex Al and deploy it to a Vertex Al endpoint Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint and then uploads predictions to a BigQuery table.
D) Import the model into BigQuery Implement the data processing logic in a SQL query On Vertex Al Pipelines create a pipeline that uses the BigqueryQueryJobop and the EigqueryPredictModejobOp components.

Google Professional Machine Learning Engineer Exam - Topic 4 Question 118 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 118
Topic #: 4
[All Professional Machine Learning Engineer Questions]

You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

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Suggested Answer: C

Vertex AI is a service that allows you to create and train ML models using Google Cloud technologies. You can use Vertex AI to import the model that you trained with TensorFlow and store it in the Vertex AI Model Registry. The Vertex AI Model Registry is a service that allows you to store and manage your ML models on Google Cloud. You can then use Vertex AI Pipelines to create a pipeline that uses the DataflowPythonJobOp and the ModelBatchPredictOp components. The DataflowPythonJobOp component is a component that allows you to run a Dataflow job using a Python script. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use the DataflowPythonJobOp component to reuse the data processing logic that you created for transforming the data into TFRecords. The ModelBatchPredictOp component is a component that allows you to run a batch prediction job using a model from the Vertex AI Model Registry. Batch prediction is a type of prediction that provides high-throughput responses to large batches of input data. You can use the ModelBatchPredictOp component to make predictions using the TFRecords from the DataflowPythonJobOp component and the model from the Vertex AI Model Registry. You can also configure the ModelBatchPredictOp component to automatically upload the predictions to a BigQuery table. BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to store and analyze the predictions from your model. You can also schedule the pipeline to run on a weekly basis, so that the predictions are updated regularly. By using Vertex AI, Vertex AI Pipelines, Dataflow, and BigQuery, you can productionize the model and upload the predictions to a BigQuery table on a weekly schedule.Reference:

Vertex AI documentation

Vertex AI Pipelines documentation

Dataflow documentation

BigQuery documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


by Kayleigh at Jun 20, 2026, 12:20 PM

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