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?

1. A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
2. B. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
3. C. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
4. D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Correct Answer: B

You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

1. A. Use the interleave option for reading data
2. B. Reduce the value of the repeat parameter
3. C. Increase the buffer size for the shuffle option.
4. D. Set the prefetch option equal to the training batch size
5. E. Decrease the batch size argument in your transformation

Correct Answer: AD

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

1. A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
2. B. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
3. C. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
4. D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Correct Answer: A

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?

1. A. Use the Natural Language API to classify support requests
2. B. Use AutoML Natural Language to build the support requests classifier
3. C. Use an established text classification model on Al Platform to perform transfer learning
4. D. Use an established text classification model on Al Platform as-is to classify support requests

Correct Answer: D

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: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

1. A. Categorical hinge
2. B. Binary cross-entropy
3. C. Categorical cross-entropy
4. D. Sparse categorical cross-entropy

Correct Answer: B