Azure Data Scientists Associate Practice Exam

What must you do to use the TensorFlow estimator for training in Azure?

1. Define a parameter for local_folder

2. Invoke the TensorFlow estimator directly

3. Use a scikit-learn estimator for preprocessing

4. Create a unique data processing pipeline

The correct answer is: Invoke the TensorFlow estimator directly

To effectively utilize the TensorFlow estimator for training in Azure, invoking the TensorFlow estimator directly is essential. The TensorFlow estimator serves as a high-level interface that enables seamless integration of TensorFlow models into the Azure Machine Learning framework. By utilizing this estimator, you can easily configure, train, and deploy TensorFlow models with built-in functionality for managing distributed training, hyperparameter tuning, and model evaluation. Direct invocation means that the estimator automatically handles the setup required to initiate training on the specified compute resources in Azure. This includes managing the communication between various services and ensuring that the appropriate TensorFlow environment and dependencies are installed and configured. While other options provide valuable processes and tools in machine learning workflows, they do not provide the direct approach needed for utilizing the TensorFlow estimator in Azure. For instance, defining a local folder, using a scikit-learn estimator for preprocessing, or creating a unique data processing pipeline may be part of the broader machine learning efforts but do not specifically relate to the direct use of the TensorFlow estimator itself.