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NEW QUESTION: 1
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Random sampling
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Incorrect Answers:
B: Grid sampling can be used if your hyperparameter space can be defined as a choice among discrete values and if you have sufficient budget to exhaustively search over all values in the defined search space. Additionally, one can use automated early termination of poorly performing runs, which reduces wastage of resources.
Example, the following space has a total of six samples:
from azureml.train.hyperdrive import GridParameterSampling
from azureml.train.hyperdrive import choice
param_sampling = GridParameterSampling( {
"num_hidden_layers": choice(1, 2, 3),
"batch_size": choice(16, 32)
}
)
B. Grid sampling
C. No sampling
D. Bayesian sampling
Answer: A,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION: 2
A. Microsoft Desktop Optimization Pack (MDOP)
B. Windows Enterprise Upgrade
C. Windows Companion Subscription License (CSL)
D. Windows Embedded
Answer: D
NEW QUESTION: 3
You are managing a SharePoint farm. The SharePoint databases are configured to run on a dedicated instance of Microsoft SQL Server 2008 R2 Service Pack 1.You need to optimize query executions. What should you do?
A. Move the SharePoint databases to a RAID 10 volume.
B. Set the maximum degree of parallelism for SQL Server instances to 0.
C. Set the maximum degree of parallelism for SQL Server instances to 1.
D. Set the maximum degree of parallelism for SQL Server instances to 4.
E. Move the master database to a RAID 10 volume.
F. Set the maximum degree of parallelism for SQL Server instances to 32.
G. Move the master database to a RAID 5 volume.
H. Move the SharePoint databases to a RAID 5 volume.
Answer: A
NEW QUESTION: 4
お客様が実行管理特権または基になるEC2インスタンスを保持できるサービスはどれですか？
（2つ選択してください。）
A. Amazon Elastic Cache
B. Elastic Load Balancing
C. Amazon Relational Database Service
D. AWS Elastic Beanstalk
E. Amazon Elastic Map Reduce
Answer: D,E
