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NEW QUESTION: 1
In which Customizing activity do you decide which special G/L transactions are to be paid automatically
via the payment program?
A. Set Up All Company Codes for Payment Transactions
B. Set Up Payment Methods per Company Code for Payment Transactions
C. Set Up Paying Company Codes for Payment Transactions
D. Set Up Payment Methods per Country for Payment Transactions
Answer: A

NEW QUESTION: 2
Universal Containers (UC) currently uses the org development model and utilizes the Salesforce CLI as the deployment tool. After the feature release artifact (a .zip file) has been tested in a lower sandbox, it is being deployed to the full sandbox for performance testing and production deployment readiness check-Since quick deployment options are not being used, what is the correct way to deploy the artifact to the full sandbox?
A. Authorize to the Full sandbox org; Validate with sfdx:mdapi:deploy; On successful validation, deploy with sfdx:source:deploy
B. Authorize to the Full sandbox; validate with sfdx: source: deploy; On successful validation, deploy with sfdx;mdapi;deploy
C. Authorize to the Full sandbox org; Validate with sfdx:source:deploy; On successful validation, deploy with sfdx:source:deploy
D. Authorize to the Full sandbox org; Validate with sfdximdapi:deploy; On successful validation, deploy with sfdx:mdapi:deploy
Answer: B

NEW QUESTION: 3
Which command produced the output shown in the exhibit?
A. Show interfaces extensive
B. Show interfaces summary
C. Show interfaces filters
D. Show interfaces terse
Answer: D

NEW QUESTION: 4
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters