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NEW QUESTION 43
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?
- A. Multi-category classification
- B. Binary classification
- C. Regression classification
- D. Streaming classification
NEW QUESTION 44
Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?
- A. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
- B. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
- C. Run a hyperparameter tuning job on AI Platform using custom containers.
- D. Convert the model to a Keras model, and run a Keras Tuner job.
NEW QUESTION 45
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?
- A. Categorical cross-entropy
- B. Binary cross-entropy
- C. Categorical hinge
- D. Sparse categorical cross-entropy
– **Categorical entropy** is better to use when you want to **prevent the model from giving more importance to a certain class**. Or if the **classes are very unbalanced** you will get a better result by using Categorical entropy.
– But **Sparse Categorical Entropy** is a more optimal coice if you have a huge amount of classes, enough to make a lot of memory usage, so since sparse categorical entropy uses less columns it **uses less memory**.
NEW QUESTION 46
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