Google professional-machine-learning-engineer

Topic 1: Professional Machine Learning Engineer

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an

image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that

new data can be fed into your ML models. You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your

test dataset. What should you do?

Topic 1: Professional Machine Learning Engineer

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?

Topic 1: Professional Machine Learning Engineer

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly

to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to

follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

Topic 1: Professional Machine Learning Engineer

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest

available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual

intervention while having version control for your code. What should you do?

Topic 1: Professional Machine Learning Engineer

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the

pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now

have to train a model with the following label map: [`˜drivers_license', `˜passport', `˜credit_card']. Which loss function should you use?