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NEW QUESTION: 1
Section B (2 Mark)
Ram born in 1950 has a life expectancy at birth of 65 years. Sita his wife born in 1955 has a life expectancy at birth of 70 years. Assuming that the life expectancies have not changed. Ram is planning to buy an annuity to be paid to him or his wife till anyone of them is alive. Assuming Ram will retire on attaining age 58 i.e. in
2008, what should be the time period of the annuity?
A. 12 years
B. 7 years
C. 17 years
D. 10 years
Answer: C

NEW QUESTION: 2
An application you maintain consists of multiple EC2 instances in a default tenancy VPC. This application has undergone an internal audit and has been determined to require dedicated hardware for one instance. Your compliance team has given you a week to move this instance to single-tenant hardware.
Which process will have minimal impact on your application while complying with this requirement?
A. Create a new VPC with tenancy=dedicated and migrate to the new VPC
B. Right click on the instance, select properties and check the box for dedicated tenancy
C. Use ec2-reboot-instances command line and set the parameter "dedicated=true"
D. Stop the instance, create an AMI, launch a new instance with tenancy=dedicated, and terminate the old instance
Answer: A
Explanation:
http://docs.aws.amazon.com/AWSEC2/latest/CommandLineReference/ApiReference-cmd-CreateVpc.html

NEW QUESTION: 3
一連のラベル付き写真を使用するマルチクラス画像分類の深層学習モデルがあります。次のコードを作成して、モデルをトレーニングするときにハイパーパラメーター値を選択します。

次の各ステートメントについて、ステートメントがtrueの場合は[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

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