Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  Dataflow 2  is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataflow supports both batch and streaming data processing pipelines. However, if your use case requires real-time inference, you need to ensure that the data preprocessing logic is applied consistently between training and serving. One way to achieve this is to refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline, and use the same code in the endpoint. This way, you can avoid data skew and drift issues that might arise from using different preprocessing methods for training and serving. Therefore, option B is the best way to ensure the data preprocessing logic is applied consistently between training and serving. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Dataflow

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

 Vertex AI Platform is a unified platform for building and deploying ML models on Google Cloud. It supports both custom and AutoML models, and provides various tools and services for ML development, such as Vertex Pipelines, Vertex Vizier, Vertex Explainable AI, and Vertex Feature Store. Vertex AI Platform allows users to train their TensorFlow models using distributed training, which can speed up the training process and handle large datasets. Vertex AI Platform also minimizes code refactoring and infrastructure overhead, as it is compatible with TensorFlow Estimators and handles the provisioning, configuration, and scaling of the training resources automatically. The other options are not as suitable for this scenario. Dataproc is a service that allows users to create and run data processing pipelines using Apache Spark and Hadoop, but it is not designed for TensorFlow model training. Managed Instance Groups are a feature that allows users to create and manage groups of identical compute instances, but they require more configuration and management than Vertex AI Platform. Kubeflow Pipelines are a tool that allows users to create and run ML workflows on Google Kuber netes Engine, but they involve more complexity and code changes than Vertex AI Platform.  References:

Vertex AI Platform documentation

Distributed training with Vertex AI Plat form

Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system.  Vertex AI Pipelines is a service that allows you to create, run, and manage ML wor kflows using TensorFlow Extended (TFX) components or custom components 1 .  App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible environments 2 .  However, App Engine does not support Docker containers in the standard environment, and does not provide a de dicated service for online prediction and monitoring of ML models 3 .

Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system.  Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with supp ort for autoscaling and custom containers 4 .  Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies 5 .

Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.

Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers.  However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the stand ard environment or online prediction and monitoring of ML models 3 .