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

To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:

Use Vertex Explainable AI with the sampled Shapley method.  Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction 1 .  The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets 2 .  The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features 3 .

Enable Vertex AI Model Monitoring to check for feature distribution drift.  Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time 4 . Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.

 In this scenario, the goal is to create an ML model to predict which newly uploaded videos will be the most popular on a video sharing website. The result that should be used to determine whether the model is successful is the one that best aligns with the business objective and the evaluation metric. Option C is the correct answer because it defines the most popular videos as the ones that have the highest watch time within 30 days of being uploaded, and it sets a high accuracy threshold of 95% for the model prediction.

Option C: The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded. This option is the best result for the scenario because it reflects the business objective and the evaluation metric. The business objective is to prioritize the videos that will attract and retain the most viewers on the website. The watch time is a good indicator of the viewer engagement and satisfaction, as it measures how long the viewers watch the videos. The 30-day window is a reasonable time frame to capture the popularity trend of the videos, as it accounts for the initial interest and the viral potential of the videos. The 95% accuracy threshold is a high standard for the model prediction, as it means that the model can correctly identify 95 out of 100 of the most popular videos based on the watch time metric.

Option A: The model predicts videos as popular if the user who uploads them has over 10,000 likes. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the users who upload them. The number of likes that a user has is not a good indicator of the popularity of their videos, as it does not measure the viewer engagement or satisfaction with the videos. Moreover, this option does not specify a time frame or an accuracy threshold for the model prediction, making it vague and unreliable.

Option B: The model predicts 97.5% of the most popular clickbait videos measured by number of clicks. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most misleading or sensational titles or thumbnails. The number of clicks that a video has is not a good indicator of the popularity of the video, as it does not measure the viewer engagement or satisfaction with the video content. Moreover, this option only focuses on the clickbait videos, which may not represent the majority or the diversity of the videos on the website.

Option D: The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most consistent or inconsistent number of views over time. The Pearson correlation coefficient is a metric that measures the linear relationship between two variables, not the popularity of the videos. A correlation coefficient of 0 means that there is no linear relationship between the log-transformed number of views after 7 days and 30 days, which does not indicate whether the videos are popular or not. Moreover, this option does not specify a threshold or a target value for the correlation coefficient, making it meaningless and irrelevant.

Auto scaling is a feature that allows you to automatically adjust the number of prediction nodes based on the traffic and load of your deployed model 1 .  However, auto scaling depends on the CPU utilization of your prediction nodes, which is the percentage of CPU resources used by your model server 1 .  If your CPU utilization is low, even during periods of high load, it means that your model server is not fully utilizing the available CPU resources, and thus au to scaling will not trigger more replicas 2 .

On e possible reason for low CPU utilization is that your model server is using a single worker process to handle prediction requests 3 .  A worker process is a subprocess that runs your model code and handles prediction requests 3 .  If you have only one worker process, it can only handle one request at a time, which can lead to dropped requests when the traffic is high 3 .  To increase the CPU utilization and the throughput of your model server, you can increase the number of worker processes, which will allow your model server to handle multiple requests in parallel 3 .

To increase the number of workers i n your model server, you need to modify your custom model server code and use the  --workers  flag to specify the number of worker processes you want to use 3 . For example, if you are using a Gunicorn server, you can use the following command to start your model server with four worker processes:

gunicorn --bind :$PORT --workers 4 --threads 1 --timeout 60 main:app

By increasing the number of workers in your model server, you can increase the CPU utilization of your prediction nodes, and thus enable auto scaling to scale beyond one replica.

The other options are not suitable for your scenario, because they either do not address the root cause of low CPU utilization, such as attaching a GPU or scheduling scaling, or they do not enable auto scaling, such as increasing the minReplicaCount, which is a fixed number of nodes that will always run regardless of the traffi c 1 .