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

To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the  aiplatform.log_metrics  function to log the F1 score and  aiplatform.log_classification_metrics  function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices.  References : The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.

Vertex AI Python SDK reference | Google Cloud

Logging custom metrics | Vertex AI

Migrating from scikit-learn to TensorFlow | TensorFlow

 The Cloud DLP API is a service that allows users to inspect, classify, and de-identify sensitive data. It can be used to scan data in Cloud Storage, BigQuery, Cloud Datastore, and Cloud Pub/Sub. The best way to ensure that the PII is not accessible by unauthorized individuals is to use a quarantine bucket to store the data before scanning it with the DLP API. This way, the data is isolated from other applications and users until it is classified and moved to the appropriate bucket. The other options are not as secure or efficient, as they either expose the data to BigQuery before scanning, or scan the data after writing it to a non-sensitive bucket.  References:

Cloud DLP documentation

Scanning and classifying Cloud Storage files

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models 1 .  You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases 1 .  You can also deploy models to endpoints, which are resources that provide a service URL for online prediction 2 .

By importing the new model to the same Vertex AI Model Registry as a different version of the existing model, you can keep track of the model versions and compare their performance metrics 1 .  You can also use aliases to label the model versions according to their readiness for production, such as  default  or  staging 1 .

By deploying the new model to the same Vertex AI endpoint as the existing model, you can use traffic splitting to gradually shift the production traffic from the old model to the new model 2 .  Traffic splitting is a feature that allows you to specify the percentage of prediction requests that each deployed model in an endpoint should handle 2 .  This way, you can minimize the impact to the existing and future model users, and monitor the performance of the new model over time 2 .

The other options are not suitable for your scenario, because they either require creating a separate endpoint or a Cloud Run service, which would increase the complexity and maintenance of your deployment, or they do not allow you to use traffic splitting, which would create a sudden change in your prediction results.  References :

Introduction to Vertex AI Model Registry | Google Cloud

Deploy a model to an endpoint | Vertex AI | Google Cloud

This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location.  References:

[Al Platform: Batch prediction overview]

[Al Platform: Exporting a SavedModel for prediction]