An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key. Which solution will meet these requirements?
An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket.
The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours.
Which solution will meet these requirements?
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
