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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
For more info read reference:
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Prerequisites
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
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Who should take the Professional Machine Learning Engineer - Google
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.
The Google Professional-Machine-Learning-Engineer exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Design and run experiments |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift |
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
Google Professional Machine Learning Engineer Sample Questions:
You work for a gaming company that develops and manages a popular massively multiplayer online (MMO) game. The game's environment is open-ended, and a large number of positions and moves can be taken by a player. Your team has developed an ML model with TensorFlow that predicts the next move of each player. Edge deployment is not possible, but low-latency serving is required. How should you configure the deployment?
- A. Use AI Platform Prediction with a high-CPU machine type to get a batch prediction for the players.
- B. Use AI Platform Prediction with a high-memory machine type to get a batch prediction for the players.
- C. Use AI Platform Prediction with a NVIDIA GPU to make real-time predictions.
- D. Use a Cloud TPU to optimize model training speed.
Correct Answer: C 🗳️
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You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic. You plan to direct all user traffic to the new model. You need to deploy the model with minimal disruption to your application. What should you do?
- A. 1. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry
2. Deploy the new model to the existing endpoint - B. 1. Create a new endpoint
2. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry
3. Deploy the new model to the new endpoint
4. Update Cloud DNS to point to the new endpoint - C. 1. Create a new endpoint
2. Create a new model. Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version. Upload the model to Vertex AI Model Registry
3. Deploy the new model to the new endpoint, and set the new model to 100% of the traffic. - D. 1. Create a new model. Set the parentModel parameter to the model ID of the currently deployed model. Upload the model to Vertex AI Model Registry.
2. Deploy the new model to the existing endpoint, and set the new model to 100% of the traffic
Correct Answer: D 🗳️
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100,000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training, and are stored in a Cloud Storage bucket. You need to be able to tune the model during each training run. How should you train the model?
- A. Train a model for object detection by using Vertex AI AutoML.
- B. Develop the model training code for image classification, and train a model by using Vertex AI custom training.
- C. Develop the model training code for object detection, and train a model by using Vertex AI custom training.
- D. Train a model for image classification by using Vertex AI AutoML.
Correct Answer: B 🗳️
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?
- A. Create a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark- bigquery-connector to access the tables.
- B. Create a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
- C. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.
- D. Create a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables.
Correct Answer: B 🗳️
Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?
- A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
- B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
- C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
- D. MirroredStrategy; Custom tier with a single master node and four v100 GPUs.
Correct Answer: D 🗳️
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