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NEW QUESTION: 1
Note: This question is part of 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.
A company has custom ASP.NET and Java applications that run old versions of Windows and Linux. The company plans to place applications in containers.
You need to design a solution that includes networking, service discovery, and load balancing for the applications. The solution must support storage orchestration.
Solution: You deploy each application to an Azure Container instance.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Docker Containers are the global standard and are natively supported in Azure, offering enterprises an interesting and flexible way to migrate legacy apps for both future proofing and cost benefits.
Containers are modular and portable. Docker containers are supported on any server operating system (Linux and Windows), in any major public cloud (Microsoft Azure, Amazon AWS, Google, IBM), and in on-premises and private or hybrid cloud environments.
References:
https://docs.microsoft.com/en-us/dotnet/standard/modernize-with-azure-and-containers/modernize-existing-apps-to-cloud-optimized/deploy-existing-net-apps-as-windows-containers
NEW QUESTION: 2
ホームページのページ読み込みパフォーマンスに基づいてトリガーされるアラートを作成する予定です。
次の図に示すApplication Insightsログクエリがあります。
ドロップダウンメニューを使用して、図に示されている情報に基づいて各ステートメントを完了する回答を選択します。
注:それぞれの正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: percentile_duration_95
Box 2: success
For example -
requests
| project name, url, success
| where success == "False"
This will return all the failed requests in my App Insights within the specified time range.
Reference:
https://devblogs.microsoft.com/premier-developer/alerts-based-on-analytics-query-using-custom-log-search/
NEW QUESTION: 3
The datacenter manager is reviewing a problem with a humidity factor that is too low. Which of the following environmental problems may occur?
A. Dry-pipe fire suppression
B. Condensation
C. Static electricity
D. EMI emanations
Answer: C
Explanation:
Humidity control prevents the buildup of static electricity in the environment. If the humidity drops much below 50 percent, electronic components are extremely vulnerable to damage from electrostatic shock.
Incorrect Answers:
A. EMI emanations are a result of failed shielding not low humidity.
C. Condensation is a result of a failed HVAC system
D. Dry-pipe fire suppression refers to results when using either fire extinguishers or preventing fires.
References:
Dulaney, Emmett and Chuck Eastton, CompTIA Security+ Study Guide, 6th Edition, Sybex, Indianapolis, 2014, p. 383
NEW QUESTION: 4
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation

Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample