Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are a machine learning engineer at an international manufacturing organization that ships scientific products globally. Each product comes with instruction manuals that must be translated into 15 different languages. Currently, your organization relies on manual human translations, which are costly and slow. The leadership team has asked you to implement a machine learning solution to reduce translation costs and increase speed. The solution should be scalable, maximize translation accuracy, and minimize operational overhead. Additionally, you need a process to evaluate and address incorrect translations. What should you do?
A
Create a workflow using Cloud Function triggers. Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket. Configure another Cloud Function that translates the documents using the Cloud Translation API, and saves the translations to an output Cloud Storage bucket. Use human reviewers to evaluate the incorrect translations.
B
Create a Vertex AI pipeline that processes the documents, launches an AutoML Translation training job, evaluates the translations and deploys the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between training and live data, re-trigger the pipeline with the latest data.
C
Use AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.
D
Use Vertex AI custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data. Deploy the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.