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

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Google Professional-Machine-Learning-Engineer Exam Overview :

Exam Name: Google Professional Machine Learning Engineer
Exam Code: Professional-Machine-Learning-Engineer Dumps
Vendor: Google Certification: Machine Learning Engineer
Questions: 285 Q&A's Shared By: emil
Question 72

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

Options:

A.

Option A 72

B.

Option B 72

C.

Option C 72

D.

Option D 72

Discussion
Question 73

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

Options:

A.

Create alerts to monitor for skew, and retrain the model.

B.

Perform feature selection on the model, and retrain the model with fewer features

C.

Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

D.

Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Discussion
Question 74

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user’s recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

Options:

A.

Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

B.

Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production.

C.

Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

D.

Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model.

Discussion
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Question 75

You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

Options:

A.

Redaction, reproducibility, and explainability

B.

Traceability, reproducibility, and explainability

C.

Federated learning, reproducibility, and explainability

D.

Differential privacy federated learning, and explainability

Discussion
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