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No doubt the Google Professional-Machine-Learning-Engineer certification is a valuable credential that offers countless advantages to Professional-Machine-Learning-Engineer exam holders. Beginners and experienced professionals can validate their skills and knowledge level with the Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Exam and earn solid proof of their proven skills.

Google Professional Machine Learning Engineer is a certification exam offered by Google Cloud. It is designed to test the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer Exam is intended for individuals who have experience in machine learning and wish to demonstrate their proficiency in designing and implementing machine learning models using Google Cloud technologies.

For more info read reference:

Google Web Services Website

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Google Professional Machine Learning Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.
B. Use Vertex Explainable Al for model explainability Configure example-based explanations.
C. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.
D. Use Vertex Explainable Al for model explainability Configure feature-based explanations.

Answer: A

Explanation:
Vertex AI Model Monitoring is a service that allows you to monitor the performance and quality of your ML models in production. You can use Vertex AI Model Monitoring to detect changes in the input data distribution, the prediction output distribution, or the model accuracy over time. Training-serving skew detection is a feature of Vertex AI Model Monitoring that compares the statistics of the data used for training the model and the data used for serving the model. If there is a significant difference between the two data distributions, it indicates that the model might be outdated or inaccurate. By enabling training-serving skew detection for your model, you can detect model performance changes in production and trigger retraining or redeployment of your model as needed. This way, you can ensure that your model is always up-to-date and accurate, without moving your data to the cloud. References:
* Vertex AI Model Monitoring documentation
* Training-serving skew detection documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 34
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

A. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
B. Build your custom container to run jobs on Al Platform Training
C. Build your custom containers to run distributed training jobs on Al Platform Training
D. Use a built-in model available on Al Platform Training

Answer: C

Explanation:
"ML framework and related dependencies are not supported by Al Platform Training" use custom containers "your model and your data are too large to fit in memory on a single machine " use distributed learning techniques

NEW QUESTION # 35
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.
What should you do?

A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run.
B. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories.
Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories.
Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic.
D. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

Answer: D

Explanation:
GCP recommends to use Cloud Build when building KubeFlow Pipelines. It's possible to run unit tests in Cloud Build. And, the others seems overly complex/unnecessary.
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture

NEW QUESTION # 36
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Vertex AI Pipelines and Al Platform Prediction
B. Cloud Composer, BigQuery ML , and Al Platform Prediction
C. Vertex AI Pipelines and App Engine
D. Cloud Composer, Al Platform Training with custom containers, and App Engine

Answer: A

Explanation:
Vertex AI Pipelines and AI Platform Prediction are the platform components that best suit the requirements of the data science team. Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning workflows using pipelines. Pipelines are portable and scalable ML workflows that are based on containers. You can use Vertex AI Pipelines to schedule model retraining, use custom containers, and integrate with other Google Cloud services. AI Platform Prediction is a service that allows you to host your trained models and serve online predictions. You can use AI Platform Prediction to deploy models trained on Vertex AI or elsewhere, and benefit from features such as autoscaling, monitoring, logging, and explainability. Reference:
Vertex AI Pipelines
AI Platform Prediction

NEW QUESTION # 37
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

A. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
B. There is training-serving skew in your production environment.
C. There is not a sufficient amount of training data.
D. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.

Answer: B

NEW QUESTION # 38
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