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NEW QUESTION: 1
Solutions Architect는 AWS 외부에서 호스팅 되는 여러 웹 서버에서 제공하는 고 가용성 웹 사이트를 설계하고 있습니다. 인스턴스가 응답하지 않으면 Architect는 회전에서 인스턴스를 제거해야 합니다.
이 요구 사항을 충족하는 가장 효율적인 방법은 무엇입니까?
A. Amazon CloudWatch를 사용하여 사용률을 모니터링합니다.
B. Amazon Elastic Load Balancer를 사용하십시오.
C. Amazon Route 53 상태 확인을 사용하십시오.
D. Amazon API Gateway를 사용하여 가용성을 모니터링하십시오.
Answer: C
Explanation:
Explanation
https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/dns-failover-simple-configs.html

NEW QUESTION: 2
DRAG DROP
John works as a Network Administrator for Blue Well Inc. The company uses Windows Vista operating system. He wants to configure the firewall access for specific programs. What steps will he take to accomplish the task?
Select and Place:

Answer:
Explanation:

Explanation/Reference:
Explanation:
A firewall is a set of related programs configured to protect private networks connected to the Internet from intrusion. It is used to regulate the network traffic between different computer networks. It permits or denies the transmission of a network packet to its destination based on a set of rules. A firewall is often installed on a separate computer so that an incoming packet does not get into the network directly.

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py