Professional-Machine-Learning-Engineer Exam Dumps - Google Professional Machine Learning Engineer

 Online inference is a process where you send a single or a small number of predict ion requests to a model and get immediate responses 1 . Online inference is suitable for scenarios where you need timely predictions, such as detecting cheating in online games.  Online inference requires that the model is deployed to an endpoint, which is a resource that prov ides a service URL for prediction requests 2 .

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models 3 .  You can import models from various sources, such as custom models or AutoML models, and assign them to different versions and aliases 3 .  You can also deploy models to endpoints, which are resources that provide a service URL for online prediction 2 .

By importing the model into Vertex AI Model Registry, you can leverage the Vertex AI features to monitor and update the model 3 . You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model’s prediction.

By creating a Vertex AI endpoint that hosts the model, you can use the Vertex AI Prediction service to serve online inference requests 2 .  Vertex AI Prediction provides various benefits, such as scalability, re liability, security, and logging 2 .  You can use the Vertex AI API or the Google Cloud console to send online inference requests to the endpoint and get immediate classifications 4 .

Therefore, the best option for your scenario is to import the model into Vertex AI Model Registry, create a Vertex AI endpoint that hosts the model, and make online inference requests.

The other options are not suitable for your scenario, because they either do not provide immediate classifications, such as using batch prediction or loading the model files each time, or they do not use Vertex AI Prediction, which would require more development and maintenance effort, such as creating a Cloud Function or a VM.

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

The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization. Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction. The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:

Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.

Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training.

Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training.  References :

Vertex AI Training | Google Cloud

Kubeflow on Google Cloud | Google Cloud

TFJob for training TensorFlow models with Kubernetes | Kubeflow

Compute Engine | Google Cloud

Slurm Workload Manager

Option A is incorrect because creating a new model that uses the raw data and is available in real time would require retraining the model and deploying it again, which is not efficient or scalable.

Option B is incorrect because using a Dataflow job to transform the incoming data would introduce unnecessary latency and complexity for online prediction, which requires fast and simple processing.

Option C is incorrect because using Cloud Spanner to stream and query the incoming data would incur high costs and overhead for online prediction, which does not need a relational database.

Option D is correct because using a Cloud Function to preprocess the data and submit a prediction request to Al Platform is a simple and scalable solution for online prediction, which leverages the serverless and event-driven features of Cloud Functions.

 According to the official exam guide 1 , one of the skills assessed in the exam is to “track the lineage of pipeline artifacts”.  Vertex ML Metadata 2  is a service that allows you to store, query, and visualize metadata associated with your ML workflows, such as datasets, models, metrics, and executions. Vertex ML Metadata helps you track the provenance and lineage of your ML artifacts and understand the relationships between them.  Vertex AI Experiments 3  is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn.  Vertex AI TensorBoard 4  is a service that allows you to visualize and monitor your ML experiments using TensorBoard, an open source tool for ML visualization. Vertex AI TensorBoard helps you track the model’s training parameters and the metrics per training epoch, and compare the performance of each version of the model. Therefore, option C is the best way to determine which Vertex AI services to include in the workflow for the given use case. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Vertex ML Metadata

Vertex AI Experiments

Vertex AI TensorBoard

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Model retraining is the process of updating an existing machine learning model with new data and parameters to improve its performance and accuracy. Model retraining is essential for maintaining the relevance and validity of the model, especially when the data or the environment changes over time.  Model retraining can help to avoid or reduce the effects of model degradation, which is the phenomenon of the model’s predictive performance decreasing as it is tested on new datasets within rapidly evolving environments 1 .

For the use case of predicting sales numbers, model accuracy is crucial, because the production model is required to keep up with market changes. Market changes can affect the demand, supply, price, and preference of the products, and thus influence the sales numbers. If the model is not retrained with new data that reflects the market changes, it may become outdated and inaccurate, and fail to capture the patterns and trends of the sales numbers. Therefore, the most likely issue that is causing the steady decline in model accuracy is the lack of model retraining.

The other options are not as likely as option B, because they are not directly related to the model’s ability to adapt to market changes. Option A, poor data quality, may affect the model’s accuracy, but it is not a specific cause of model degradation over time. Option C, too few layers in the model for capturing information, may affect the model’s complexity and expressiveness, but it is not a specific cause of model degradation over time. Option D, incorrect data split ratio during model training, evaluation, validation, and test, may affect the model’s generalization and validation, but it is not a specific cause of model degradation over time. Therefore, option B, lack of model retraining, is the best answer for this question.

Precision  measures the fraction of messages flagged by the model that are actually inappropriate, while  recall  measures the fraction of inappropriate messages that are flagged by the model. These metrics are useful for evaluating how well the model can identify and filter out inappropriate comments.

Option A is not a good metric because it does not account for the accuracy of the model. The model might flag many messages that are not inappropriate, or miss many messages that are inappropriate.

Option B is better than option A, but it still does not account for the recall of the model. The model might flag only a few messages that are highly likely to be inappropriate, but miss many other messages that are less obvious but still inappropriate.

Option C is not a good metric because it does not focus on the messages that are flagged by the model. The random sample of 0.1% of raw messages might contain very few inappropriate messages, making the precision and recall estimates unreliable.