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NEW QUESTION: 1
You manage a public-facing web application which allows authenticated users to upload and download large files. On the initial public page there is a promotional video.
You plan to give users access to the site content and promotional video. In the table below, identify the access method that should be used for the anonymous and authenticated parts of the application.
Make only one selection in each column.

Answer:
Explanation:

Explanation

Anonymous: If a SAS is published publicly, it can be used by anyone in the world.
Authenticated: Create an Access Policy..
Not Ad-Hoc: Ad-Hoc would have to have a time limit.
References: https://docs.microsoft.com/en-in/azure/storage/storage-dotnet-shared-access-signature-part-1

NEW QUESTION: 2
いくつかの会社の従業員は、特定の30日間に1年に1回アプリケーションを使用します。期間は会社ごとに異なります。これらの30日間、アプリケーションへのトラフィックが急増します。
これらのトラフィックスパイクを処理するために、アプリケーションをどのように設計できますか？
A. Auto Scalingグループを使用して、サイトトラフィックに一致するようにEC2インスタンスの数をスケーリングします
B. Amazon S3を使用して、ウェブサイトリクエストの静的要素をキャッシュします
C. Amazon CloudFrontを使用して静的アセットを提供し、EC2インスタンスの負荷を減らします
D. Amazon Route 53を使用して、ウェブサイトリクエストの静的要素をキャッシュします
Answer: B

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 are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins