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NEW QUESTION 33
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

  • A. Perform feature selection on the model, and retrain the model with fewer features
  • B. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
  • C. Create alerts to monitor for skew, and retrain the model.
  • D. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

Answer: D

 

NEW QUESTION 34
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

  • A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
  • B. Use Dataflow with the SavedModel to read the data from BigQuery
  • C. Export the model to BigQuery ML.
  • D. Deploy and version the model on Al Platform.

Answer: C

Explanation:
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#bq CREATE OR REPLACE MODEL example_dataset.imported_tf_model OPTIONS (MODEL_TYPE=’TENSORFLOW’, MODEL_PATH=’gs://cloud-training-demos/txtclass/export/exporter/1549825580/*’)

 

NEW QUESTION 35
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

  • A. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
  • B. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  • C. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  • D. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.

Answer: A

 

NEW QUESTION 36
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