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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

Answer: A

 

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.

Answer: B

 

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

Answer: A

Explanation:
– **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**.
https://stats.stackexchange.com/questions/326065/cross-entropy-vs-sparse-cross-entropy-when-to-use-one-over-the-other

 

NEW QUESTION 46
……

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