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NEW QUESTION: 1
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Permutation Feature Importance
B. Filter Based Feature Selection
C. Fisher Linear Discriminant Analysis.
D. Synthetic Minority Oversampling Technique (SMOTE)
Answer: D
Explanation:
Explanation
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION: 2
You have a Dockerfile that you need to deploy on Kubernetes Engine. What should you do?
A. Create a docker image from the Dockerfile and upload it to Cloud Storage. Create a Deployment YAML file to point to that image. Use kubectl to create the deployment with that file.
B. Use kubectl app deploy <dockerfilename>.
C. Create a docker image from the Dockerfile and upload it to Container Registry. Create a Deployment YAML file to point to that image. Use kubectl to create the deployment with that file.
D. Use gcloud app deploy <dockerfilename>.
Answer: C
Explanation:
Reference https://cloud.google.com/kubernetes-engine/docs/tutorials/hello-app
NEW QUESTION: 3
How many bits make up an IPv6 address?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: A