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NEW QUESTION: 1
You plan to upgrade your computer to Windows 7.
You need to view a report that identifies potential compatibility problems with your computer's hardware, devices, and programs.
Which tool should you use?
A. Windows Anytime Upgrade
B. Windows Upgrade Advisor
C. Windows Easy Transfer
D. Sync Center
Answer: B
NEW QUESTION: 2
Auto Scalingグループにトラフィックを保持しながら、トラフィックを送信する前に新しいインスタンスをテストする場合、どのAuto Scalingプロセスが役立ちますか？
A. プロセスAZリバランスを中断します
B. プロセスAddToLoadBalancerを中断します
C. プロセスヘルスチェックを一時停止します
D. プロセスの一時停止
Answer: B
Explanation:
Explanation
If you suspend Ad dTo Load Balancer, Auto Scaling launches the instances but does not add them to the load balancer or target group. If you resume the AddTo Load Balancer process. Auto Scaling resumes adding instances to the load balancer or target group when they are launched. However, Auto Scaling does not add the instances that were launched while this process was suspended. You must register those instances manually.
Option A is invalid because this just balances the number of CC2 instances in the group across the Availability Zones in the region Option B is invalid because this just checks the health of the instances. Auto Scaling marks an instance as unhealthy if Amazon CC2 or Clastic Load Balancing tells Auto Scaling that the instance is unhealthy.
Option C is invalid because this process just terminates instances that are marked as unhealthy and later creates new instances to replace them.
For more information on process suspension, please refer to the below document link: from AWS
* http://docs.aws.amazon.com/autoscaling/latest/userguide/as-suspend-resume-processes.html
NEW QUESTION: 3
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