Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

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

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

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?

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


Get Premium Professional Machine Learning Engineer Questions as Interactive Web-Based Practice Test or PDF Real Exam Format Detailed Explanations Instant Access Get Your Practice Test
12 comments · newest first
MO
Mona 2 months ago
I’m leaning towards A. Vertex AI Pipelines look powerful for automation.
ME
Melvin 2 months ago
I agree, B seems efficient. Directly uploads to BigQuery too.
LA
Lauran 2 months ago
I think option B is the best. It keeps the data processing logic intact.
FR
Freeman 2 months ago
I’m skeptical about D, SQL for model predictions? Really?
QU
Quiana 2 months ago
Totally agree with B, it keeps everything streamlined!
EF
Effie 2 months ago
Wait, can you really import models directly into BigQuery? Sounds odd.
DA
Daniela 3 months ago
I think C could work too, but not sure about the components.
LI
Linwood 3 months ago
Option B seems like the best choice for integration.
CH
Charolette 3 months ago
I’m a bit confused about option D. Importing the model into BigQuery seems different from what we usually do. Is that even a common approach?
LA
Lawrence 3 months ago
I feel like option C could be a good fit too, especially since it talks about batch predictions. We did a similar exercise in our last session.
JA
Jaclyn 3 months ago
I'm not entirely sure, but I remember something about Vertex AI endpoints being important for deploying models. Maybe option A is the right choice?
LU
Lura 4 months ago
I think option B sounds familiar because it mentions reusing the data processing logic, which is something we practiced in class.
0/2000 characters

Save Cancel