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Google Professional-Machine-Learning-Engineer
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You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You anticipate changes in the feature distributions and the correlations between the features in the near future You also expect to receive a large volume of prediction requests You plan to use Vertex Al Model Monitoring for drift detection and you want to minimize the cost. What should you do?
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With the complete collection of Professional-Machine-Learning-Engineer practice test, Exams4sure has assembled to take you through Machine Learning Engineer test questions for your Google exam preparation. In this Professional-Machine-Learning-Engineer exam dumps study guide we have compiled real Google Professional Machine Learning Engineer exam questions with their answers so that you can prepare and pass Machine Learning Engineer exam in your first attempt.
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A Professional Machine Learning Engineer in the realm of Google Cloud is tasked with designing, building, and deploying scalable machine learning models and systems that leverage Google Cloud's infrastructure and services.
ML Engineers utilize Google Cloud's robust data storage and processing capabilities, such as BigQuery, Dataflow, and Dataproc, to efficiently manage and analyze large and intricate datasets for machine learning tasks.
Throughout the ML model development process, a Machine Learning Engineer is responsible for tasks like data preprocessing, feature engineering, model selection, hyperparameter tuning, training, evaluation, and deployment.
ML Engineers collaborate closely with data scientists, software developers, DevOps engineers, and business stakeholders to understand requirements, integrate machine learning solutions into existing systems, and ensure that ML-based applications meet business objectives.
Machine Learning Engineers need proficiency in programming languages like Python and experience with data platforms such as TensorFlow, PyTorch, Scikit-learn, and Google Cloud's suite of ML services like AutoML and AI Platform.
ML Engineers play a pivotal role in democratizing machine learning by developing reusable components, best practices, and tools that empower teams across the organization to build and deploy ML models efficiently and effectively, thus fostering innovation and driving business growth.
TESTED 27 Jul 2024
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