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NEW QUESTION: 1
あなたの会社は、AndroidデバイスとOSデバイスをターゲットにしたモバイルアプリを構築しています。チームはAzure DevOpsを使用して、すべての作業項目とリリースサイクルを管理します。以下のタスクを実行するための解決策を提案する必要があります。
* 問題分析のためにクラッシュレポートを収集する
* あなたのテスターに​​ベータ版を配布する。
* 新しいアプリの機能に関するユーザーからのフィードバックを受け取る。
あなたは推薦に何を含めるべきですか？
A. Jenkinsの統合
B. Microsoft Test＆Feedback拡張
C. Azure Application Insightsウィジェット
D. Microsoft Visual Studio App Centerとの統合
Answer: B
Explanation:
Explanation
The "Exploratory Testing" extension is now "Test & Feedback" and is now Generally Available.
Anyone can now test web apps and give feedback, all directly from the browser on any platform: Windows, Mac, or Linux. Available for Google Chrome and Mozilla Firefox (required version 50.0 or above) currently.
Support for Microsoft Edge is in the pipeline and will be enabled once Edge moves to a Chromium-compatible web platform.
References:
https://marketplace.visualstudio.com/items?itemName=ms.vss-exploratorytesting-web

NEW QUESTION: 2
At the end of the fiscal period, the account debited to show the estimated amount of uncollectible accounts is
A. None of these answers is correct.
B. Accounts Receivable
C. Allowance for Uncollectible Accounts
D. Bad Debt Expense
E. Unearned Revenue
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The adjusting entry should recognize an expense and increase the allowance account.

NEW QUESTION: 3
You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run. Which Python code segment should you use?
A)

B)

C)

D)

A. Option C
B. Option B
C. Option D
D. Option A
Answer: D
Explanation:
The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.
In [ ]:
best_run, fitted_model = local_run.get_output()
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb