Microsoft AZ-400 Practice Test - Questions Answers, Page 19

Azure Platform as a Service (PaaS) resources, like Azure SQL and Web Sites (Web Apps), can emit performance metrics data natively to Log Analytics. The Premium plan will retain up to 12 months of data, giving you an excellent baseline ability.

There are two options available in the Azure portal for analyzing data stored in Log analytics and for creating queries for ad hoc analysis. Incorrect Answers:

B: Intelligent Insights analyzes database performance by comparing the database workload from the last hour with the past seven-day baseline workload. However, we need handle application metrics as well.

Reference: https://docs.microsoft.com/en-us/azure/azure-monitor/platform/collect-azurepass-posh

After setting up Application Insights for your project, and if your app generates a certain minimum amount of data, Smart Detection of failure anomalies takes 24 hours to learn the normal behavior of your app, before it is switched on and can send alerts.

Reference: https://docs.microsoft.com/en-us/azure/azure-monitor/app/proactive-failure-diagnostics

Enable Remote Debugging

Before we start a debugging session to our Azure Function app we need to enable the functionality.

1. Navigate in the Azure portal to your function app fa-11566895

2. Go to the “Application settings”

3. Under “Debugging” set Remote Debugging to On and set Remote Visual Studio version to 2017.

Reference:

https://www.locktar.nl/uncategorized/azure-remote-debugging-manually-in-visual-studio-2017/

There are several tools available for running queries in Azure Data Explorer, including Kusto.

Kusto uses a role-based access control (RBAC) model, under which authenticated principals are mapped to roles, and get access according to the roles they're assigned. Note: Azure Data Explorer is a highly scalable and secure analytics service that enables you to do rich exploration of structured and unstructured data for instant insights. Optimized for ad-hoc queries, Azure Data Explorer enables rich data exploration over raw, structured, and semi-structured data delivering fast time to insight. Query with a modern, intuitive query language that offers fast, ad-hoc, and advanced query capabilities over high-rate data volumes and varieties

Reference: https://docs.microsoft.com/en-us/azure/data-explorer/tools-integrations-overview

Performing user interface (UI) testing as part of the release pipeline is a great way of detecting unexpected changes, and need not be difficult. Selenium can be used to test your website during a continuous deployment release and test automation.

Reference: https://docs.microsoft.com/en-us/azure/devops/pipelines/test/continuous-test-selenium?view=azure-devops

Sampling is a feature in Azure Application Insights. It is the recommended way to reduce telemetry traffic, data costs, and storage costs, while preserving a statistically correct analysis of application data.

The Application Insights SDK for ASP.NET Core supports both fixed-rate and adaptive sampling. Adaptive sampling is enabled by default.

D: For adaptive sampling: The volume is adjusted automatically to keep within a specified maximum rate of traffic, and is controlled via the setting MaxTelemetryItemsPerSecond. If the application produces a low amount of telemetry, such as when debugging or due to low usage, items won't be dropped by the sampling processor as long as volume is below MaxTelemetryItemsPerSecond.

Note: In ApplicationInsights.config, you can adjust several parameters in the AdaptiveSamplingTelemetryProcessor node. The figures shown are the default values:

<MaxTelemetryItemsPerSecond>5</MaxTelemetryItemsPerSecond>

Reference:

https://docs.microsoft.com/en-us/azure/azure-monitor/app/sampling

Monitoring your application health is an important signal for managing and upgrading your deployment. Azure virtual machine scale sets provide support for rolling upgrades including automatic OS-image upgrades, which rely on health monitoring of the individual instances to upgrade your deployment. You can also use health extension to monitor the application health of each instance in your scale set and perform instance repairs using automatic instance repairs.

Reference:

https://docs.microsoft.com/en-us/azure/virtual-machine-scale-sets/virtual-machine-scale-sets-health-extension

D: You can mark or unmark a test as flaky based on analysis or context, by choosing Flaky.

To configure flaky test management, choose Project settings, and select Test management in the Pipelines section.

B:

Slide the On/Off button to On.

Reference:

https://docs.microsoft.com/en-us/azure/devops/pipelines/test/flaky-test-management

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.

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.

Reference:

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