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”.  Dat aproc 2  is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it.  You can also use Vertex AI Workbench 3 , a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Dataproc

Vertex AI Workbench

Google Professional Machine Learning Certification Exam 2023

Latest Google Profe ssional Machine Learning Engineer Actual Free Exam Questions

Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task.  BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries 1 .  You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression 2 .  You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve 3 .  BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools 4 .

Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task.  Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools 5 . SparkML is a library that provides ML algorithms and utilities for Spark. However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.

Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.

Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.

Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data.  F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class 1 .  Precision is the fraction of true p ositives among all predicted positives, while recall is the fraction of true positives among all actual positives 1 . When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company’s logo, which are rare but important to detect.  By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included 2 .

Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data.  RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values 3 .  RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete 4 .

Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data.  F1 score is a special case of F-score where precision and recall are equally weighted 1 .  F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent 5 .  However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 sc ore may not reflect the performance of the model well 2 .

Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data.  By weighing precision more than recall, we can emphasize the impo rtance of minimizing the false positives, even if some true positives are missed 2 .  However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well 2 .