Machine Learning Engineer Professional-Machine-Learning-Engineer Passing Score

 The problem of the text classifier is that it has a high false positive rate for comments that reference certain underrepresented religious groups. This means that the classifier is not able to distinguish between toxic and non-toxic language when those groups are mentioned. One possible reason for this is that the training data does not have enough examples of non-toxic comments that reference those groups, leading to a biased model. Therefore, a possible solution is to add synthetic training data where those phrases are used in non-toxic ways, which can help the model learn to generalize better and reduce the false positive rate. Synthetic data is artificially generated data that mimics the characteristics of real data, and can be used to augment the existing data when the real data is scarce or imbalanced. References:

• Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week 3: Fairness
• Google Cloud Professional Machine Learning Engineer Exam Guide, Section 4: Ensuring solution quality, 4.4 Evaluating fairness and bias in ML models
• Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9: Responsible AI, Section 9.3: Fairness and Bias

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

 Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to log metrics and parameters from your TensorFlow models, and then visualize them in Vertex AI TensorBoard. Vertex AI TensorBoard is a managed service that provides a web interface for viewing and debugging your ML experiments. You can use Vertex AI TensorBoard to compare different runs, inspect model graphs, analyze scalars, histograms, images, and more. By using Vertex AI Experiments and Vertex AI TensorBoard, you can simplify your ML experiment tracking and visualization workflow, and avoid the overhead of setting up and maintaining your own Cloud Functions, Cloud Storage buckets, or VMs. References:

• [Vertex AI Experiments documentation]
• [Vertex AI TensorBoard documentation]
• Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

 According to the official exam guide1, one of the skills assessed in the exam is to “automate and orchestrate ML pipelines using Cloud Composer”. Cloud Composer2 is a fully managed workflow orchestration service that uses Apache Airflow to create, schedule, monitor, and manage workflows. Cloud Composer allows you to build custom training pipelines for deep learning-based models and integrate them with other Google Cloud services. You can also use Cloud Composer to implement model interpretability techniques, such as feature attributions, explainable AI, or model debugging3. The other options are not relevant or optimal for this scenario. References:

• Professional ML Engineer Exam Guide
• Cloud Composer
• Model interpretability with Cloud Composer
• Google Professional Machine Learning Certification Exam 2023
• Latest Google Professional Machine Learning Engineer Actual Free Exam Questions