Professional-Machine-Learning-Engineer Exam Dumps : Google Professional Machine Learning Engineer

Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task.  BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries 1 .  You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression 2 .  You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve 3 .  BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools 4 .

Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task.  Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools 5 . SparkML is a library that provides ML algorithms and utilities for Spark. However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.

Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.

Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.

Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data.  F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class 1 .  Precision is the fraction of true p ositives among all predicted positives, while recall is the fraction of true positives among all actual positives 1 . When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company’s logo, which are rare but important to detect.  By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included 2 .

Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data.  RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values 3 .  RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete 4 .

Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data.  F1 score is a special case of F-score where precision and recall are equally weighted 1 .  F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent 5 .  However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 sc ore may not reflect the performance of the model well 2 .

Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data.  By weighing precision more than recall, we can emphasize the impo rtance of minimizing the false positives, even if some true positives are missed 2 .  However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well 2 .

 Overfitting occurs when a model tries to fit the training data so closely that it does not generalize well to new data. Overfitting can be caused by having a model that is too complex for the data, such as having too many parameters or layers.  Overfitting can lead to poor performance on the validation data, which reflects how the model will perform on unseen data 1

To prevent overfitting, one strategy is to use regularization techniques that penalize the complexity of the model and encourage it to learn simpler patterns. Two common regularization techniques for deep neural networks are L2 regularization and dropout. L2 regularization adds a term to the loss function that is proportional to the squared magnitude of the model’s weights. This term penalizes large weights and encourages the model to use smaller weights. Dropout randomly drops out some units in the network during training, which prevents co-adaptation of features and reduces the effec tive number of parameters.  Both L2 regularization and dropout have hyperparameters that control the strength of the regularization effect 2 3

Another strategy to prevent overfitting is to use hyperparameter tuning, which is the process of finding the optimal values for the parameters of the model that affect its performance. Hyperparameter tuning can help find the best combination of hyperparameters that minimize the validation loss and improve the generalization ability of the model. AI Platform provides a service for hyperparameter tuning that can run multiple trials in parallel and use different search algorithms to find the best solution.

Therefore, the best strategy to use when retraining the model is to run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters. This will allow the model to find the optimal balance between fitting the training data and generalizing to new data. The other options are not as effective, as they either use fixed values for the regularization parameters, which may not be optimal, or they do not address the issue of overfitting at all.