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

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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: ajay
Question 16

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

Options:

A.

Increase the instance memory to 512 GB and increase the batch size.

B.

Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C.

Enable early stopping in your Vertex AI Training job.

D.

Use the tf.distribute.Strategy API and run a distributed training job.

Discussion
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Question 17

Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

Options:

A.

Add synthetic training data where those phrases are used in non-toxic ways

B.

Remove the model and replace it with human moderation.

C.

Replace your model with a different text classifier.

D.

Raise the threshold for comments to be considered toxic or harmful

Discussion
Question 18

You recently deployed a pipeline in Vertex Al Pipelines that trains and pushes a model to a Vertex Al endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

Options:

A.

Set up a CI/CD pipeline that builds and tests your source code If the tests are successful use the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex Al Pipelines.

B.

Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment Run unit tests in the pre-production environment If the tests are successful deploy the pipeline to production.

C.

Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment deploy the pipeline to production

D.

Set up a CI/CD pipeline that builds and tests your source code and then deploys built arrets into a pre-production environment After a successful pipeline run in the pre-production environment, rebuild the source code, and deploy the artifacts to production

Discussion
Question 19

You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

Options:

A.

Use AutoML to optimize the model’s recall in order to minimize false negatives.

B.

Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.

C.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

D.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

Discussion
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