Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

 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 prepare the data for your customer churn prediction model, such as filtering, aggregating, and transforming the data. You can then associate the data with a Vertex AI dataset, which is a service that allows you to store and manage your ML data on Google Cloud. By using a Vertex AI dataset, you can easily access the data from other Vertex AI services, such as AutoML. AutoML is a service that allows you to create and train ML models without writing code. You can use AutoML to create an AutoMLTabularTrainingJob, which is a type of job that trains a classification model for tabular data, such as customer churn. By using an AutoMLTabularTrainingJob, you can benefit from the automated feature engineering, model selection, and hyperparameter tuning that AutoML provides. You can also use Vertex Explainable AI to understand how your model is making predictions, such as which features are most important and how they affect the prediction outcome. By using BigQuery, Vertex AI dataset, and AutoMLTabularTrainingJob, you can build your first model as quickly as possible while minimizing cost and complexity. References:

BigQuery documentation

Vertex AI dataset documentation

AutoMLTabularTrainingJob documentation

Vertex Explainable AI documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Feature attribution helps to determine how each feature influences predictions, essential for identifying bias. Vertex AI’s built-in explainability tools provide insights without altering the model’s feature space. Model monitoring (Option A) detects distributional drift rather than feature influence. Options B and D do not directly address the request to explain model decisions or provide fairness insights.

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AI Platform supports hyperparameter tuning for PyTorch models using custom containers. This allows you to use any Python dependencies and libraries that are not included in the pre-built AI Platform Training runtime versions. You can also use a pre-trained model such as ResNet as a base for your custom model. To run a hyperparameter tuning job on AI Platform using custom containers, you need to do the following steps:

Create a Dockerfile that defines the container image for your training application. The Dockerfile should install PyTorch and any other dependencies, copy your training code and configuration files, and set the entrypoint for the container.

Build the container image and push it to Container Registry or another accessible registry.

Create a YAML file that defines the configuration for your hyperparameter tuning job. The YAML file should specify the container image URI, the training input and output paths, the hyperparameters to tune, the metric to optimize, and the tuning algorithm and budget.

Submit the hyperparameter tuning job to AI Platform using the gcloud command-line tool or the AI Platform Training API.

References:

Hyperparameter tuning overview

Using custom containers

PyTorch on AI Platform Training