Free Google Professional-Machine-Learning-Engineer Exam Dumps Questions & Answers

You are a lead ML architect at a small company that is migrating from on-premises to Google Cloud. Your company has limited resources and expertise in cloud infrastructure. You want to serve your models from Google Cloud as soon as possible. You want to use a scalable, reliable, and cost-effective solution that requires no additional resources. What should you do?

• A.  Configure Compute Engine VMs to host your models.
• B.  Deploy your models on Vertex AI endpoints.
• C.  Create a managed cluster on Google Kubernetes Engine (GKE), and deploy your models as containers.
• D.  Create a Cloud Run function to deploy your models as serverless functions.

Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

• A.  One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
• B.  MirroredStrategy; Custom tier with a single master node and four v100 GPUs.
• C.  Default Strategy; Custom tier with a single master node and four v100 GPUs.
• D.  One Device Strategy; Custom tier with a single master node and four v100 GPUs.

You recently created a new Google Cloud project. After testing that you can submit a Vertex AI Pipeline job from the Cloud Shell, you want to use a Vertex AI Workbench user-managed notebook instance to run your code from that instance. You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?

• A.  Ensure that the Vertex AI Workbench instance is assigned the Identity and Access Management (IAM) Vertex AI User role.
• B.  Ensure that the Vertex AI Workbench instance is assigned the Identity and Access Management (IAM) Notebooks Runner role.
• C.  Ensure that the Workbench instance that you created is in the same region of the Vertex AI Pipelines resources you will use.
• D.  Ensure that the Vertex AI Workbench instance is on the same subnetwork of the Vertex AI Pipeline resources that you will use.

You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per hour than expected throughout the day. You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency. What should you do?

• A.  Configure an appropriate minReplicaCount value based on expected baseline traffic
• B.  Deploy two models to the same endpoint, and distribute requests among them evenly
• C.  Change the model's machine type to one that utilizes GPUs
• D.  Set the target utilization percentage in the autoscailngMetricSpecs configuration to a higher value

You want to migrate a scikit-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 AI 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?

• A.  Use the aiplatform.log_classification_metrics function to log the F1 score, and use the aiplatform.log_metrics function to log the confusion matrix.
• B.  Use the aiplatform.log_classification_metrics function to log the F1 score and the confusion matrix.
• C.  Use the aiplatform.log_metrics function to log the F1 score and the confusion matrix.
• D.  Use the aiplatform.log_metrics function to log the F1 score: and use the aiplatform.log_classification_metrics function to log the confusion matrix.