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NEW QUESTION: 1
Your Azure Machine Learning workspace has a dataset named . A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto

NEW QUESTION: 2
Your company hosts a social media website for storing and sharing documents. The web application allows user to upload large files while resuming and pausing the upload as needed. Currently, files are uploaded to your PHP front end backed by Elastic Load Balancing and an autoscaling fleet of Amazon Elastic Compute Cloud (EC2) instances that scale upon average of bytes received (NetworkIn). After a file has been uploaded, it is copied to Amazon Simple Storage Service (S3). Amazon EC2 instances use an AWS Identity and Access Management (IAM) role that allows Amazon S3 uploads. Over the last six months, your user base and scale have increased significantly, forcing you to increase the Auto Scaling group's Max parameter a few times.
Your CFO is concerned about rising costs and has asked you to adjust the architecture where needed to better optimize costs.
Which architecture change could you introduce to reduce costs and still keep your web application secure and scalable?
A. Replace the Auto Scaling launch configuration to include c3.8xlarge instances; those instances can potentially yield a network throughput of 10gbps.
B. Re-architect your ingest pattern, and move your web application instances into a VPC public subnet.
Attach a public IP address for each EC2 instance (using the Auto Scaling launch configuration settings).
Use Amazon Route 53 Round Robin records set and HTTP health check to DNS load balance the app requests; this approach will significantly reduce the cost by bypassing Elastic Load Balancing.
C. Re-architect your ingest pattern, have the app authenticate against your identity provider, and use your identity provider as a broker fetching temporary AWS credentials from AWS Secure Token Service (GetFederationToken). Securely pass the credentials and S3 endpoint/prefix to your app. Implement client-side logic that used the S3 multipart upload API to directly upload the file to Amazon S3 using the given credentials and S3 prefix.
D. Re-architect your ingest pattern, have the app authenticate against your identity provider, and use your identity provider as a broker fetching temporary AWS credentials from AWS Secure Token Service (GetFederationToken). Securely pass the credentials and S3 endpoint/prefix to your app. Implement client-side logic to directly upload the file to Amazon S3 using the given credentials and S3 prefix.
Answer: B

NEW QUESTION: 3
Which statement is true about 1NR, 2NR, and 2CR SureMark compatibility mode operation?
A. 1NR. 2NR. and 2CR printer compatibility mode is limited to TGCS 4690 OS users.
B. Without changing drivers 1NR, 2NR. and 2CR printers can operate as legacy 4610 printers.
C. Compatibility mode provides the same 1NR, 2NR, 2CR printer function as when operating in native mode.
D. With available application updates 1NR, 2NR. and 2CR printers will operate as legacy 4610 printers.
Answer: A

NEW QUESTION: 4
IT 보안 부서에서는 기본 EC2 인스턴스 용으로 생성 된 EBS 볼륨의 모든 데이터를 암호화해야 합니다. 다음 중 이것을 달성하는 데 도움이 될 수있는 것은 무엇입니까?
선택 해주세요:
A. AWS Certificate Manager
B. 오전 1시 액세스 키
C. STS가있는 API 게이트웨이
D. AWS KMS API
Answer: D
Explanation:
설명
AWS 설명서는 AWS KMS에서 다음을 언급합니다.
AWS KMS (AWS Key Management Service)는 데이터를 암호화하는데 사용되는 암호화 키를 쉽게 생성하고 제어 할 수있는 관리 형 서비스입니다. AWS KMS는 Amazon EBS (Amazon Elastic Block Store), Amazon Simple Storage Service (Amazon S3), Amazon Redshift Amazon Elastic Transcoder, Amazon WorkMail, Amazon Relational Database Service (Amazon RDS) 등의 다른 AWS 서비스와 통합되어 있습니다. 옵션 B를 관리하는 암호화 키로 데이터를 암호화하는 것이 간단 함-AWS Certificate Manager를 사용하여 트래픽 전송을 암호화하는 데 사용할 수있는 SSL 인증서를 생성 할 수 있지만 휴지 상태가 아닙니다. 옵션 C가 올바르지 않습니다. 토큰 발행에 다시 사용됩니다 전송중인 트래픽에 API 게이트웨이를 사용하는 경우
옵션 D는 EC2 인스턴스에 대한 보안 액세스에 사용됩니다
AWS KMS에 대한 자세한 내용은 다음 URL을 방문하십시오.
https://docs.aws.amazon.com/kms/latest/developereuide/overview.htmll
정답은 다음과 같습니다. AWS KMS API
전문가에게 피드백 / 질문을 제출하십시오