Google Professional-Machine-Learning-Engineer valid study dumps : Google Professional Machine Learning Engineer

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex AI endpoint, and validated that results were received in a reasonable amount of time. After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests.
What should you do?

• A. Use a machine type with more memory
• B. Decrease the CPU utilization target in the autoscaling configurations
• C. Decrease the number of workers per machine
• D. Increase the CPU utilization target in the autoscaling configurations.

You are developing a machine learning pipeline using the Kubeflow Pipelines (KFP) SDK that will run on Agent Platform Pipelines. The pipeline includes a preprocessing component that takes two hours to transform a large dataset. You are currently iterating on the code for the subsequent model_training component and need to re-submit the pipeline frequently for testing. You need to ensure that the preprocessing component only re-runs if its inputs or parameters have changed.
You want to minimize effort, costs, and total execution time. What should you do?

• A. Ensure that execution caching is enabled for the preprocessing component by including the set_caching_options(enable_caching=True) method.
• B. Configure the pipeline to write the preprocessed data to a persistent volume claim (PVC) that is shared across pipeline runs.
• C. Track the run lineage using Agent Platform Experiments, and pass the output artifact URI from the previous run into the training component.
• D. Add a dsl.Condition step to the pipeline that checks if the preprocessing step has executed in the last hour.

You are building an ML model to predict customer churn for a subscription service. You have trained your model on Vertex AI using historical data, and deployed it to a Vertex AI endpoint for real-time predictions. After a few weeks, you notice that the model's performance, measured by AUC (area under the ROC curve), has dropped significantly in production compared to its performance during training. How should you troubleshoot this problem?

• A. Monitor the resource utilization of the endpoint, such as CPU and memory usage, to identify potential bottlenecks in performance.
• B. Enable Vertex Explainable AI feature attribution to analyze model predictions and understand the impact of each feature on the model's predictions.
• C. Monitor the training/serving skew of feature values for requests sent to the endpoint.
• D. Monitor the latency of the endpoint to determine whether predictions are being served within the expected time frame.

You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

• A. Posts can be compared to the keyword list much more quickly.
• B. A much longer keyword list can be used to flag spam posts.
• C. Spam posts can be flagged using far fewer keywords.
• D. New problematic phrases can be identified in spam posts.

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories. You have a labeled dataset in Cloud Storage. You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency. What should you do?

• A. Train the model by using AutoML, and register the model in Vertex AI Model Registry. Configure your mobile application to send batch requests during prediction.
• B. Train the model by using AutoML, and expose the model as a Vertex AI endpoint. Configure your mobile application to invoke the endpoint during prediction.
• C. Train the model by using AutoML Edge, and export the model as a TFLite model. Configure your mobile application to use the .tflite file directly.
• D. Train the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.