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NEW QUESTION: 1
ネットワークには、Server1という名前のメンバーサーバーを含むcontoso.comという名前のオンプレミスのActiveDirectoryドメインが含まれています。
次の表に示すアカウントがあります。

Server1にAzureADConnectをインストールしています。
Azure ADConnect同期のアカウントを指定する必要があります。ソリューションは、最小特権の原則を使用する必要があります。
どのアカウントを指定する必要がありますか？
A. CONTOSO \ User1
B. CONTOSO \ User2
C. CONTOSO \ User3
D. SERVER1 \ User4
Answer: B
Explanation:
Explanation
The default Domain User permissions are sufficient
Reference:
https://docs.microsoft.com/en-us/azure/active-directory/hybrid/reference-connect-accounts-permissions

NEW QUESTION: 2
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.
You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?

A. Option A
B. Option E
C. Option B
D. Option D
E. Option C
Answer: C
Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Incorrect Answers:
A: Pandas DataFrame not used.
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets

NEW QUESTION: 3
Why should an administrator avoid allocating over half the RAM on a host to a FSVM?
A. The FSVM has a set memory.configuration.
B. The FSVM would experience host swapping of memory
C. The FSVM would cross NUMA node boundaries
D. The FSVM would be in a single NUMA node boundary
Answer: A

NEW QUESTION: 4
회사에는 AWS에 정의 된 EBS 볼륨 세트가 있습니다. 보안 의무는 모든 EBS 볼륨이 암호화되어야 한다는 것입니다. 계정에 암호화되지 않은 볼륨이 있는 경우 IT 관리자에게 알리기 위해 수행 할 수 있는 작업.
선택 해주세요:
A. AWS Lambda를 사용하여 암호화되지 않은 EBS 볼륨을 확인하십시오.
B. AWS Config를 사용하여 암호화되지 않은 EBS 볼륨 확인
C. AWS Inspector를 사용하여 모든 EBS 볼륨을 검사
D. AWS Guard Duty를 사용하여 암호화되지 않은 EBS 볼륨을 확인하십시오.
Answer: B
Explanation:
The enc
config rule for AWS Config can be used to check for unencrypted volumes.
encrypted-volurrn
5 volumes that are in an attached state are encrypted. If you specify the ID of a KMS key for encryptio using the kmsld parameter, the rule checks if the EBS volumes in an attached state are encrypted with that KMS key*1.
Options A and C are incorrect since these services cannot be used to check for unencrypted EBS volumes Option D is incorrect because even though this is possible, trying to implement the solution alone with just the Lambda servk would be too difficult For more information on AWS Config and encrypted volumes, please refer to below URL:
https://docs.aws.amazon.com/config/latest/developerguide/encrypted-volumes.html Submit your Feedback/Queries to our Experts