Google Updated Professional-Machine-Learning-Engineer Exam Questions and Answers by ronny

• Quantization is a technique that reduces the numerical precision of the weights and activations of a neural network, which can improve the inference speed and reduce the memory footprint of the model1.
• Reducing the floating point precision from tf.float64 to tf.float16 can potentially halve the latency and memory usage of the model, while having minimal impact on the accuracy2.
• Increasing the dropout rate to 0.8 in either mode would not affect the latency, but would likely degrade the performance of the model significantly, as dropout is a regularization technique that randomly drops out units during training to prevent overfitting3.
• Switching from CPU to GPU serving may or may not improve the latency, depending on the hardware specifications and the model complexity, but it would also incur additional costs and complexity for deployment4

 XGBoost is a popular open-source library that provides a scalable and efficient implementation of gradient boosted trees. You can use XGBoost to train a classification or regression model on tabular data. You can also use Vertex AI to productionize the model and expose it for internal use as an HTTP microservice. Vertex AI is a service that allows you to create and train ML models using Google Cloud technologies. You can use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex AI Endpoints. A prebuilt Vertex container is a container image that contains the dependencies and libraries needed to run a specific ML framework, such as XGBoost. You can use a prebuilt Vertex container to simplify the model creation and deployment process, without having to build your own custom container. Vertex AI Endpoints is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoints to deploy the model from the prebuilt Vertex container and expose it as an HTTP microservice. You can also configure the endpoint to handle a small number of incoming requests, and optimize the latency and cost of serving the model. By using a prebuilt XGBoost Vertex container and Vertex AI Endpoints, you can productionize the model with the least amount of effort and latency. References:

• XGBoost documentation
• Vertex AI documentation
• Prebuilt Vertex container documentation
• Vertex AI Endpoints documentation
• Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

 The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1

One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature. Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2

Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3

To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:

• Enable the online prediction logging for each model version that you want to evaluate. This will allow AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4
• Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval.
• View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.

The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.

References: 1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]

 AutoML Tables is a service that allows you to automatically build, analyze, and deploy machine learning models on tabular data. It is suitable for large-scale regression and classification problems, and it supports various optimization objectives, data splitting methods, and hyperparameter tuning algorithms. AutoML Tables can handle both categorical and numerical features, and it can also handle missing values and outliers. AutoML Tables is a good choice for this problem because it minimizes the effort and training time required to train a regression model, while maximizing the model performance.

RMSLE stands for Root Mean Squared Logarithmic Error, and it is a metric that measures the average difference between the logarithm of the predicted values and the logarithm of the actual values. RMSLE is useful for regression problems where the target variable can include negative values, and where large differences between small values are more important than large differences between large values. For example, RMSLE penalizes underestimating a value of 10 by 2 more than overestimating a value of 1000 by 20. RMSLE is a good optimization objective for this problem because it can handle negative values in the target variable, and it can reduce the impact of outliers and large errors.

For more information about AutoML Tables and RMSLE, see the following references:

• AutoML Tables: end-to-end workflows on AI Platform Pipelines
• Predict workload failures before they happen with AutoML Tables
• How to Calculate RMSE in R