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NEW QUESTION: 1
新しいAzure Data Factory環境があります。
過去60日間のパイプライン実行を定期的に分析して、実行期間の傾向を特定する必要があります。ソリューションでは、Azure Log Analyticsを使用してデータをクエリし、グラフを作成する必要があります。
Data Factoryでどの診断設定を構成する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Log type: PipelineRuns
A pipeline run in Azure Data Factory defines an instance of a pipeline execution.
Storage location: An Azure Storage account
Data Factory stores pipeline-run data for only 45 days. Use Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis. You can also keep them in a storage account so that you have factory information for your chosen duration.
Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
References:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor
NEW QUESTION: 2
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements:
* accounts for the performance of all previous runs when evaluating the current run
* avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Median stopping
B. Bandit
C. Default
D. Truncation selection
Answer: A,C
Explanation:
Explanation
The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.
If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.medianstoppingpolicy
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.truncationselectionpolic
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy
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 question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You need to ensure that the SecurityPin security requirements are met.
Solution: Configure the web application to connect to the database using the WebAppIdentity security prinicipal. Using the Azure Portal, add Data Masking to the SecurityPin column and exclude the WebAppIdentity service principal.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Scenario: Users' SecurityPin must be stored in such a way that access to the database does not allow the viewing of SecurityPins. The web application is the only system that should have access to SecurityPins.
All certificates and secrets used to secure data must be stored in Azure Key Vault.
Testlet 3
Case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Background
You are a developer for Proseware, Inc. You are developing an application that applies a set of governance policies for Proseware's internal services, external services, and applications. The application will also provide a shared library for common functionality.
Requirements
Policy service
You develop and deploy a stateful ASP.NET Core 2.1 web application named Policy service to an Azure App Service Web App. The application reacts to events from Azure Event Grid and performs policy actions based on those events.
The application must include the Event Grid Event ID field in all Application Insights telemetry.
Policy service must use Application Insights to automatically scale with the number of policy actions that it is performing.
Policies
Log Policy
All Azure App Service Web Apps must write logs to Azure Blob storage. All log files should be saved to a container named logdrop. Logs must remain in the container for 15 days.
Authentication events
Authentication events are used to monitor users signing in and signing out. All authentication events must be processed by Policy service. Sign outs must be processed as quickly as possible.
PolicyLib
You have a shared library named PolicyLib that contains functionality common to all ASP.NET Core web services and applications. The PolicyLib library must:
Exclude non-user actions from Application Insights telemetry.
Provide methods that allow a web service to scale itself
Ensure that scaling actions do not disrupt application usage
Other
Anomaly detection service
You have an anomaly detection service that analyzes log information for anomalies. It is implemented as an Azure Machine Learning model. The model is deployed as a web service.
If an anomaly is detected, an Azure Function that emails administrators is called by using an HTTP WebHook.
Health monitoring
All web applications and services have health monitoring at the /health service endpoint.
Policy loss
When you deploy Policy service, policies may not be applied if they were in the process of being applied during the deployment.
Performance issue
When under heavy load, the anomaly detection service undergoes slowdowns and rejects connections.
Notification latency
Users report that anomaly detection emails can sometimes arrive several minutes after an anomaly is detected.
Relevant portions of the app files are shown below. Line numbers are included for reference only and include a two-character prefix that denotes the specific file to which they belong.
Relevant portions of the app files are shown below. Line numbers are included for reference only and include a two-character prefix that denotes the specific file to which they belong.