Copyright © 2023 Russell College Design By Nubevest.
P.S. Free 2025 Google Professional-Machine-Learning-Engineer dumps are available on Google Drive shared by VCEDumps: https://drive.google.com/open?id=12d_RMrZ1RYm-B1mg_O-59KTJYq_XUE2c
As is known to us, our company has promised that the Professional-Machine-Learning-Engineer valid study guide materials from our company will provide more than 99% pass guarantee for all people who try their best to prepare for the Professional-Machine-Learning-Engineer exam. If you are preparing for the Professional-Machine-Learning-Engineer exam by the guidance of the Professional-Machine-Learning-Engineer study practice question from our company and take it into consideration seriously, you will absolutely pass the Professional-Machine-Learning-Engineer exam and get the related certification. So do not hesitate and hurry to buy our Professional-Machine-Learning-Engineer study materials!
The Professional Machine Learning Engineer exam is a performance-based assessment that evaluates the candidate's ability to solve real-world problems using machine learning techniques. Professional-Machine-Learning-Engineer exam consists of a series of hands-on tasks that require the candidate to demonstrate their understanding of various machine learning concepts and their ability to apply them in practical scenarios. Professional-Machine-Learning-Engineer Exam is conducted online and can be taken from anywhere in the world.
>> Google Professional-Machine-Learning-Engineer New Dumps <<
Free update for 365 days is available if you buy Professional-Machine-Learning-Engineer exam braindumps from us. That is to say, in the following year, you can get the latest information about the Professional-Machine-Learning-Engineer exam dumps timely. And the update version will be sent to your email automatically. In addition, the Professional-Machine-Learning-Engineer Exam Braindumps are compiled by experienced experts who are quite familiar with the dynamics about the exam center, therefore the quality and accuracy of the Professional-Machine-Learning-Engineer exam braindumps can be guaranteed.
Google Professional-Machine-Learning-Engineer (Google Professional Machine Learning Engineer) Certification Exam is a professional-level certification exam offered by Google that tests your proficiency in building and deploying machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer Exam is designed for individuals who have a solid understanding of machine learning concepts and have experience in building and deploying machine learning models on Google Cloud Platform.
NEW QUESTION # 250
You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?
Answer: C
Explanation:
The option C is the most efficient and scalable solution for deploying a machine learning workflow with multiple models while ensuring version control and minimizing compute resource utilization. By exposing each model as an endpoint in Vertex AI Endpoints, it allows for easy versioning and management of individual models. Using Cloud Run to orchestrate the workflow ensures that the application can scale down to zero, thus minimizing resource utilization when not in use. Cloud Run is a service that allows you to run stateless containers on a fully managed environment or on Google Kubernetes Engine. You can use Cloud Run to invoke the endpoints of each model in the workflow and pass the data between them. You can also use Cloud Run to handle the input and output of the workflow and provide an HTTP interface for the application. Reference:
Vertex AI Endpoints documentation
Cloud Run documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 251
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
Answer: A
Explanation:
A ResourceExhaustedError: out of memory (OOM) when allocating tensor is an error that occurs when the GPU runs out of memory while trying to allocate memory for a tensor. A tensor is a multi-dimensional array of numbers that represents the data or the parameters of a machine learning model. The size and shape of a tensor depend on various factors, such as the input data, the model architecture, the batch size, and the optimization algorithm1.
For the use case of training a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine, the best option to resolve the error is to reduce the batch size. The batch size is a parameter that determines how many input examples are processed at a time by the model. A larger batch size can improve the model's accuracy and stability, but it also requires more memory and computation. A smaller batch size can reduce the memory and computation requirements, but it may also affect the model's performance and convergence2.
By reducing the batch size, the GPU can allocate less memory for each tensor, and avoid running out of memory. Reducing the batch size can also speed up the training process, as the GPU can process more batches in parallel. However, reducing the batch size too much may also have some drawbacks, such as increasing the noise and variance of the gradient updates, and slowing down the convergence of the model. Therefore, the optimal batch size should be chosen based on the trade-off between memory, computation, and performance3.
The other options are not as effective as option B, because they are not directly related to the memory allocation of the GPU. Option A, changing the optimizer, may affect the speed and quality of the optimization process, but it may not reduce the memory usage of the model. Option C, changing the learning rate, may affect the convergence and stability of the model, but it may not reduce the memory usage of the model.
Option D, reducing the image shape, may reduce the size of the input tensor, but it may also reduce the quality and resolution of the image, and affect the model's accuracy. Therefore, option B, reducing the batch size, is the best answer for this question.
References:
* ResourceExhaustedError: OOM when allocating tensor with shape - Stack Overflow
* How does batch size affect model performance and training time? - Stack Overflow
* How to choose an optimal batch size for training a neural network? - Stack Overflow
NEW QUESTION # 252
While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?
Answer: B
NEW QUESTION # 253
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?
Answer: C
NEW QUESTION # 254
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
Answer: D
Explanation:
Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain1. Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset2. AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These models are based on state-of-the-art natural language processing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using one of these models on AI Platform, you can customize the model's code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.
Reference:
Transfer Learning - Machine Learning's Next Frontier
A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning Text classification models Text Classification with Pre-trained Models in TensorFlow
NEW QUESTION # 255
......
Reliable Professional-Machine-Learning-Engineer Test Tips: https://www.vcedumps.com/Professional-Machine-Learning-Engineer-examcollection.html
What's more, part of that VCEDumps Professional-Machine-Learning-Engineer dumps now are free: https://drive.google.com/open?id=12d_RMrZ1RYm-B1mg_O-59KTJYq_XUE2c
Campus : Level 1 190 Queen Street, Melbourne, Victoria 3000
Training Kitchen : 17-21 Buckhurst, South Melbourne, Victoria 3205
Email : info@russellcollege.edu.au
Phone : +61 399987554