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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

• 87% of Google Cloud certified individuals are more confident about their cloud skills
• Professional Cloud Architect was the highest paying certification of 2020 and 2019
• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

How to book the Professional Machine Learning Engineer - Google

To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

• Step 1: Go to the Google Official Site
• Step 2: Read the instruction carefully
• Step 3: Follow the given steps
• Step 4: Apply for the Professional Machine Learning Engineer Exam

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Monitoring
• Choose appropriate Google Cloud software components
• Automation of data preparation and model training/deployment
• Identifying potential regulatory issues
• A variety of component types - data collection; data management
• Privacy implications of data usage
• Exploration/analysis
• Building secure ML systems
• Data connections
• Serving
• Selection of quotas and compute/accelerators with components
• Optimizing data use and storage
• Design reliable, scalable, highly available ML solution
• Logging/management
• Choose appropriate Google Cloud hardware components
• Automation
• Feature engineering

Google Professional Machine Learning Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

• A. Develop an image segmentation ML model to locate the boundaries of the rust spots.
• B. Develop an image classification ML model to predict the presence of the disease.
• C. Create an object detection model that can localize the rust spots.
• D. Develop a template matching algorithm using traditional computer vision libraries.

Answer: A

NEW QUESTION # 27
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A. The model predicts the seasonality well, but not the trend.
• B. The model predicts the trend well, but not the seasonality.
• C. The model predicts both the trend and the seasonality well
• D. The model does not predict the trend or the seasonality well.

Answer: D

NEW QUESTION # 28
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

• A. Reinforcement learning
• B. Recommender system
• C. Convolutional Neural Networks (CNN)
• D. Recurrent Neural Networks (RNN)

Answer: C

NEW QUESTION # 29
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
• B. Create a Managed Instance Group with autoscaling
• C. Create a cluster on Dataproc for training
• D. Use Vertex Al Platform for distributed training

Answer: D

Explanation:
AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.

NEW QUESTION # 30
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

• A. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
• B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
• C. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
• D. Use the Al Platform custom containers feature to receive training jobs using any framework

Answer: D

Explanation:
because AI platform supported all the frameworks mentioned. And Kubeflow is not managed service in GCP. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch
https://cloud.google.com/ai-platform/training/docs/containers-overview#advantages_of_custom_containers Use the ML framework of your choice. If you can't find an AI Platform Training runtime version that supports the ML framework you want to use, then you can build a custom container that installs your chosen framework and use it to run jobs on AI Platform Training.

NEW QUESTION # 31
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