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

 The best options for adjusting the training parameters in AutoML to improve model performance are to decrease the score threshold and add more positive examples to the training set. These options can help increase the detection rate of fraudulent transactions, which is the priority for this use case. The score threshold is a parameter that determines the minimum probability score that a prediction must have to be classified as positive. Decreasing the score threshold can increase the recall of the model, which is the proportion of actual positive cases that are correctly identified. Increasing the recall can help reduce the number of false negatives, which are fraudulent transactions that are missed by the model. However, decreasing the score threshold can also decrease the precision of the model, which is the proportion of positive predictions that are actually correct. Decreasing the precision can increase the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model.  Therefore, there is a trade-off between recall and precision, and the optimal score threshold depends on the business objective and the cost of errors 1 . Adding more positive examples to the training set can help balance the data distribution and improve the model performance. Positive examples are the instances that belong to the target class, which in this case are fraudulent transactions. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Fraudulent transactions are usually rare and imbalanced compared to legitimate transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class.  Adding more positive examples can help the model learn more features and patterns of the fraudulent transactions, and increase the detection rate 2 .

The other options are not as good as options B and C, for the following reasons:

Option A: Increasing the score threshold would decrease the detection rate of fraudulent transactions, which is the opposite of the desired outcome. Increasing the score threshold would decrease the recall of the model, which is the proportion of actual positive cases that are correctly identified. Decreasing the recall would increase the number of false negatives, which are fraudulent transactions that are missed by the model. Increasing the score threshold would increase the precision of the model, which is the proportion of positive predictions that are actually correct. Increasing the precision would decrease the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model.  However, in this use case, the cost of false negatives is much higher than the cost of false positives, so increasing the score threshold is not a good option 1 .

Option D: Adding more negative examples to the training set would not improve the model performance, and could worsen the data imbalance. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Legitimate transactions are usually abundant and dominant compared to fraudulent transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class.  Adding more negative examples would exacerbate th is problem, and decrease the detection rate of the fraudulent transactions 2 .

Option E: Reducing the maximum number of node hours for training would not improve the model performance, and could limit the model optimization. Node hours are the units of computation that are used to train an AutoML model. The maximum number of node hours is a parameter that determines the upper limit of node hours that can be used for training. Reducing the maximum number of node hours would reduce the training time and cost, but also the model quality and accuracy.  Reducing the maximum number of node hours wo uld limit the number of iterations, trials, and evaluations that the model can perform, and prevent the model from finding the optimal hyperparameters and architecture 3 .

Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning (ML) workflows using pipelines 1 .  A pipeline is a description of an ML workflow, including all of the components in the workflow, how the components are connected as a graph, and the runtime parameters that the pipeline accepts 1 .  Vertex AI Pipelines helps you manage the end-to-end lifecycle of your ML projects, from data preprocessing to model deployment 1 .

Vertex AI Feature Store is a service that enables you to serve, share, and reuse ML features across different models and projects 2 .  A feature is a measurable property or characteristic of an entity, such as the age of a person or the price of a product 2 .  Vertex AI Feature Store helps you reduce data duplication, ensure data consistency, and improve model performance 2 .

Vertex AI Experiments is a service that helps you track and compare the performance of di fferent versions of your models 3 .  You can use Vertex AI Experiments to run multiple training jobs with different hyperparameters, architectures, or data sources, and then compare the results using metrics, visualizations, and reports 3 .  Vertex AI Experiments helps you identify the best model for your use case and optimize your model performance 3 .  References :

Vertex AI Pipelines | Google Cloud

Vertex AI Feature Store | Google Cloud

Vertex AI Experiments | Google Cloud