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Quiz Unparalleled Authorized Professional-Machine-Learning-Engineer Exam Dumps - Exam Google Professional Machine Learning Engineer Consultant

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Google Professional Machine Learning Engineer exam is designed to test the expertise of individuals in the field of machine learning. Google Professional Machine Learning Engineer certification provides a strong foundation in machine learning concepts and tools, as well as the ability to develop and deploy sophisticated machine learning models using Google Cloud technologies. Professional-Machine-Learning-Engineer Exam assesses one's ability to use Google’s machine learning tools and services to build and deploy robust, scalable, and efficient machine learning models.

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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

The Google Professional-Machine-Learning-Engineer Exam is designed to test a variety of skills and knowledge areas related to machine learning, including data analysis, model selection and evaluation, and deployment and monitoring of machine learning models. It is also designed to test candidates' ability to apply machine learning techniques to real-world problems and to demonstrate their ability to work effectively with data science teams.

Google Professional Machine Learning Engineer Sample Questions (Q260-Q265):

NEW QUESTION # 260
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with
8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

A. 1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert investigate the cause
B. 1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.
2 Set up a monitoring job and an alert for GPU usage.
3 If you receive an alert investigate the cause.
C. 1 Change the machine type on the endpoint to have 32 vCPUs
2. Set up a monitoring job and an alert for CPU usage
3 If you receive an alert, scale the vCPUs further as needed
D. 1, Maintain the same machine type on the endpoint.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert add a compute node to the endpoint

Answer: A

Explanation:
Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources.
You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. References:
* [Vertex AI Endpoint documentation]
* [Autoscaling documentation]
* [Monitoring documentation]
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

NEW QUESTION # 261
You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics.
You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

A. Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.
B. Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.
C. Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.
D. Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.

Answer: D

Explanation:
Feature attribution helps to determine how each feature influences predictions, essential for identifying bias.
Vertex AI's built-in explainability tools provide insights without altering the model's feature space. Model monitoring (Option A) detects distributional drift rather than feature influence. Options B and D do not directly address the request to explain model decisions or provide fairness insights.

NEW QUESTION # 262
You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production.
You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

A. Update the model monitoring job to use the more recent training data that was used to retrain the model.
B. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.
C. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint
D. Update the model monitoring job to use a lower sampling rate.

Answer: A

Explanation:
The best option for resolving the training-serving skew alert is to update the model monitoring job to use the more recent training data that was used to retrain the model. This option can help align the baseline distribution of the model monitoring job with the current distribution of the production data, and eliminate the false positive alerts. Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Model Monitoring can monitor the model's prediction input data for feature skew and drift.
Training-serving skew occurs when the feature data distribution in production deviates from the feature data distribution used to train the model. If the original training data is available, you can enable skew detection to monitor your models for training-serving skew. Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature's values in the training data. If the distance score for a feature exceeds an alerting threshold that you set, Model Monitoring sends you an email alert. However, if you retrain the model with more recent training data, and deploy it back to the Vertex AI endpoint, the baseline distribution of the model monitoring job may become outdated and inconsistent with the current distribution of the production data. This can cause the model monitoring job to generate false positive alerts, even if the model performance is not deteriorated. To avoid this problem, you need to update the model monitoring job to use the more recent training data that was used to retrain the model. This can help the model monitoring job to recalculate the baseline distribution and the distance scores, and compare them with the current distribution of the production data. This can also help the model monitoring job to detect any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade1.
The other options are not as good as option B, for the following reasons:
* Option A: Updating the model monitoring job to use a lower sampling rate would not resolve the training-serving skew alert, and could reduce the accuracy and reliability of the model monitoring job.
The sampling rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower sampling rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower sampling rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. Moreover, using a lower sampling rate would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data2.
* Option C: Temporarily disabling the alert, and enabling the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could expose the model to potential risks and errors. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a feature exceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores. Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data. Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade. This can expose the model to potential risks and errors, and affect the user satisfaction and trust1.
* Option D: Temporarily disabling the alert until the model can be retrained again on newer training data, and retraining the model again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a featureexceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores.
Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data.
Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade.
This can expose the model to potential risks and errors, and affect the user satisfaction and trust.
Retraining the model again on newer training data would create a new model version, but it would not update the model monitoring job to use the newer training data as the baseline distribution. Therefore, retraining the model again on newer training data would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.3: Monitoring ML Models
* Using Model Monitoring
* Understanding the score threshold slider
* Sampling rate

NEW QUESTION # 263
Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?

A. Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget.
B. Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available.
C. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when anomalies are detected.
D. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when feature drift is detected.

Answer: D

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "configure and optimize model monitoring jobs". Vertex AI Model Monitoring documentation states that "model monitoring helps you detect when your model's performance degrades over time due to changes in the data that your model receives or returns" and that "you can configure model monitoring to send notifications to Pub/Sub when it detects anomalies or drift in your model's predictions"2. Therefore, enabling model monitoring on the Vertex AI endpoint and configuring Pub/Sub to call the Cloud Function when feature drift is detected would help you keep the model up-to-date and minimize retraining costs. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Vertex AI Model Monitoring
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 264
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

A. Ensure that training is reproducible
B. Ensure that all hyperparameters are tuned
C. Ensure that feature expectations are captured in the schema
D. Ensure that model performance is monitored

Answer: A

Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf

NEW QUESTION # 265
......

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