| Exam Name: | AWS Certified Machine Learning Engineer - Associate | ||
| Exam Code: | MLA-C01 Dumps | ||
| Vendor: | Amazon Web Services | Certification: | AWS Certified Associate |
| Questions: | 241 Q&A's | Shared By: | emaan |
A company is using ML to predict the presence of a specific weed in a farmer ' s field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?
A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio. After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .
Check the analysis results on the SageMaker Studio console. .
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Schedule an hourly model explainability monitor.
An ML engineer is analyzing a classification dataset before training a model in Amazon SageMaker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre-training bias metric.
Which metric will meet this requirement?
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a " Disease " target column.
How should the ML engineer configure the data to train the model?