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NEW QUESTION: 1
Your company hosts a web application in Azure. The company uses Azure Pipelines for the build and release management of the application.
Stakeholders report that the past few releases have negatively affected system performance.
You configure alerts in Azure Monitor.
You need to ensure that new releases are only deployed to production if the releases meet defined performance baseline criteria in the staging environment first.
What should you use to prevent the deployment of releases that fall to meet the performance baseline?
A. a trigger
B. an Azure function
C. a gate
D. an Azure Scheduler job
Answer: C
Explanation:
Scenarios and use cases for gates include:
* Quality validation. Query metrics from tests on the build artifacts such as pass rate or code coverage and deploy only if they are within required thresholds.
Use Quality Gates to integrate monitoring into your pre-deployment or post-deployment. This ensures that you are meeting the key health/performance metrics (KPIs) as your applications move from dev to production and any differences in the infrastructure environment or scale is not negatively impacting your KPIs.
Note: Gates allow automatic collection of health signals from external services, and then promote the release when all the signals are successful at the same time or stop the deployment on timeout. Typically, gates are used in connection with incident management, problem management, change management, monitoring, and external approval systems.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/continuous-monitoring
https://docs.microsoft.com/en-us/azure/devops/pipelines/release/approvals/gates?view=azure-devops

NEW QUESTION: 2
Azure Machine Learningを使用して機械学習モデルをトレーニングしています。トレーニングスクリプトをリモートで実行するコンピューティングターゲットが必要です。次のPythonコードを実行します。


Answer:
Explanation:

Explanation:
Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
1. To see all compute targets for your workspace, use the following steps:
2. Navigate to Azure Machine Learning studio.
3. Under Manage, select Compute.
4. Select tabs at the top to show each type of compute target.

Box 3: Yes
min_nodes is not specified, so it defaults to 0.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

NEW QUESTION: 3

Which interoperability technique implemented on the router would allow Host-1 to communicate with Host-2?
A. 6to4 tunnel
B. NAT-PT
C. ISATAP tunnel
D. Dual Stack
E. GRE tunnel
Answer: B