Question 1

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?

Accepted Answer

Correct answer: A

Question 2

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?

Accepted Answer

Correct answer: D

Question 3

You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)

Accepted Answer

Correct answer: D

Question 4

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the

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results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

Accepted Answer

Correct answer: A

Question 5

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account
balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How
should you serve your predictions?

Accepted Answer

Correct answer: B

Question 6

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of
the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you
recommend to the team?

Accepted Answer

Correct answer: B

Question 7

You are developing ML models with Al 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?

Accepted Answer

Correct answer: A

Question 8

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and
deploy your system. How should you develop recommendations that increase revenue while following best practices?

Accepted Answer

Correct answer: C

Question 9

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?

Accepted Answer

Correct answer: D

Question 10

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?

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Accepted Answer

Correct answer: A