You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?
Correct Answer: B
Question 257
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture. Which of the following will accomplish this? (Choose two.)
Correct Answer: A,E
Question 258
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
Correct Answer: C
This option is the best way to architect the workflow, as it allows you to use event-driven and serverless components to automate the ML training process. Cloud Storage triggers are a feature that allows you to send notifications to a Pub/Sub topic when an object is created, deleted, or updated in a storage bucket. Pub/Sub is a service that allows you to publish and subscribe to messages on various topics. Pub/Sub-triggered Cloud Functions are a type of Cloud Functions that are invoked when a message is published to a specific Pub/Sub topic. Cloud Functions are a serverless platform that allows you to run code in response to events. By using these components, you can create a workflow that starts the training job on a GKE cluster as soon as a new file is available in the Cloud Storage bucket, without having to manage any servers or poll for changes. The other options are not as efficient or scalable as this option. Dataflow is a service that allows you to create and run data processing pipelines, but it is not designed to trigger ML training jobs on GKE. App Engine is a service that allows you to build and deploy web applications, but it is not suitable for polling Cloud Storage for new files, as it may incur unnecessary costs and latency. Cloud Scheduler is a service that allows you to schedule jobs at regular intervals, but it is not ideal for triggering ML training jobs based on data availability, as it may miss some files or run unnecessary jobs. Reference: Cloud Storage triggers documentation Pub/Sub documentation Pub/Sub-triggered Cloud Functions documentation Cloud Functions documentation Kubeflow Pipelines documentation
Question 259
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?
Correct Answer: B
Question 260
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 Al 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?
Correct Answer: D
According to the web search results, Vertex AI is a unified platform for machine learning development and deployment. Vertex AI offers various services and tools for building, managing, and serving machine learning models1. Vertex AI allows you to deploy your models to endpoints for online prediction, and configure the compute resources and autoscaling options for your deployed models2. Autoscaling with Vertex AI endpoints is (by default) based on the CPU utilization across all cores of the machine type you have specified. The default threshold of 60% represents 60% on all cores. For example, for a 4 core machine, that means you need 240% utilization to trigger autoscaling3. Therefore, if you discover that the endpoint does not autoscale as expected when receiving multiple requests, you might need to decrease the CPU utilization target in the autoscaling configurations. This way, you can lower the threshold for triggering autoscaling and allocate more resources to handle the prediction requests. Therefore, option D is the best way to solve the problem for the given use case. The other options are not relevant or optimal for this scenario. References: * Vertex AI * Deploy a model to an endpoint * Vertex AI endpoint doesn't scale up / down * Google Professional Machine Learning Certification Exam 2023 * Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
