Tools y preguntas específicas

What should you do?

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How should you implement the voice call transcription feature following Google-recommended best practices?

• Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

• Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

• Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

• Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

What should you do?

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How should you log the metrics?

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You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

• Use BigQuery’s scheduling service to run the model retraining query periodically.

•  Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.

• Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.

• Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

• Build a random forest regression model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.

• Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.

• Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.

• Build a random forest classification model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.

How should you set up your experiments?

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What should you do?

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What should you do?

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You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases. You have unstructured textual data with custom labels. You need to extract and classify various medical phrases with these labels. What should you do?

• Use the Healthcare Natural Language API to extract medical entities.

• Use a BERT-based model to fine-tune a medical entity extraction model.

• Use AutoML Entity Extraction to train a medical entity extraction model.

• Use TensorFlow to build a custom medical entity extraction model.