Most Recent Google Professional-Machine-Learning-Engineer Exam Dumps

The best business metric to track to measure the model's performance is user engagement as measured by the number of battles played daily per user. This metric reflects the main goal of the model, which is to enhance the user experience and satisfaction by creating balanced and fair battles. If the model is successful, it should increase the user retention and loyalty, as well as the word-of-mouth and referrals. This metric is also easy to measure and interpret, as it can be directly obtained from the user activity data.

The other options are not optimal for the following reasons:

A . Average time players wait before being assigned to a team is not a good metric, as it does not capture the quality or outcome of the battles. It only measures the efficiency of the model, which is not the primary objective. Moreover, this metric can be influenced by external factors, such as the availability and demand of players, the network latency, and the server capacity.

B . Precision and recall of assigning players to teams based on their predicted versus actual ability is not a good metric, as it is difficult to measure and interpret. It requires having a reliable and consistent way of estimating the player's ability, which can be subjective and dynamic. It also requires having a ground truth label for each assignment, which can be costly and impractical to obtain. Moreover, this metric does not reflect the user feedback or satisfaction, which is the ultimate goal of the model.

D . Rate of return as measured by additional revenue generated minus the cost of developing a new model is not a good metric, as it is not directly related to the model's performance. It measures the profitability of the model, which is a secondary objective. Moreover, this metric can be affected by many other factors, such as the market conditions, the pricing strategy, the marketing campaigns, and the competition.

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Google Cloud launches machine learning engineer certification

How to measure user engagement

How to choose the right metrics for your machine learning model

Parallel interleave is a technique that can improve the performance of the input pipeline by reading and processing data from multiple files in parallel. This can reduce the idle time of the GPU and speed up the training process. Parallel interleave can be implemented using the tf.data.experimental.parallel_interleave () function in TensorFlow, which takes a map function that returns a dataset for each input element, and a cycle length that determines how many input elements are processed concurrently. Parallel interleave can also handle different file sizes and processing times by using a block length argument that controls how many consecutive elements are produced from each input element before switching to another input element. For more information about parallel interleave and how to use it, see the following references:

How to use parallel_interleave in TensorFlow

Better performance with the tf.data API

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 class1.Precision is the fraction of true positives among all predicted positives, while recall is the fraction of true positives among all actual positives1. 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 included2.

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 values3.RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete4.

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 weighted1.F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent5.However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 score may not reflect the performance of the model well2.

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 importance of minimizing the false positives, even if some true positives are missed2.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 well2.

Precision, recall, and F-measure

F-score for imbalanced data

RMSE

Regression vs classification

F1 score

[Imbalanced classification]

[Binary classification]

According to the official exam guide1, one of the skills assessed in the exam is to ''configure and optimize model monitoring jobs''. The Vertex AI Model Monitoring documentation states that ''to reduce the cost of model monitoring, you can configure the sample rate of the requests that are logged and analyzed by model monitoring''. Therefore, decreasing the sample_rate parameter in the Randomsampleconfig of the monitoring job would reduce the model monitoring cost while continuing to quickly detect drift. The other options are not relevant or optimal for this scenario.Reference:

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Professional ML Engineer Exam Guide

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

[Vertex AI Model Monitoring]

NVIDIA A100 GPUs are optimized for training complex models like Stable Diffusion XL. Using float32 precision ensures high model accuracy during training, whereas float16 or bfloat16 may cause lower precision in gradients, especially important for image editing. Distributing across multiple instances with T4 GPUs (Options C and D) would not speed up the process effectively due to lower power and more complex setup requirements.