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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Windows Server 2019を実行するServer1という名前のサーバーがあります。Server1はコンテナーホストです。
コンテナーイメージを構築するDockerfileを作成しています。
Server1のFile1.txtという名前のファイルを、コンテナーイメージのC：\ Folder1という名前のフォルダーに追加する必要があります。
解決策：次の行をDockerfileに追加します。
XCOPY File1.txt C:\Folder1\
You then build the container image.
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Copy is the correct command to copy a file to the container image. Furthermore, the root directory is specified as '/' and not as 'C:/'.
References:
https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#add-or-copy
https://docs.docker.com/engine/reference/builder/

NEW QUESTION: 2
You administer a Microsoft SQL Server 2016 server that hosts a transactional database and a reporting database. The transactional database is updated through a web application and is operational throughout the day.
The reporting database is only updated from the transactional database.
The recovery model and backup schedule are configured as shown in the following table:

At 14:00 hours, you discover that pages 71, 520, and 713 on one of the database files are corrupted on the reporting database.
You need to ensure that the databases are restored.
You also need to ensure that data loss is minimal. What should you do?
A. Perform a point-in-time restore.
B. Restore the latest full backup. Then, restore each differential backup taken before the time of failure from the most recent full backup.
C. Restore the latest full backup. Then, restore the latest differential backup.
D. Restore the latest full backup.
E. Restore the latest full backup, and restore the latest differential backup. Then, restore the latest log backup.
F. Perform a page restore.
G. Perform a partial restore.
H. Restore the latest full backup, and restore the latest differential backup. Then, restore each log backup taken before the time of failure from the most recent differential backup.
Answer: C
Explanation:
At restore time, before you restore a differential backup, you must restore its base.
Then, restore only the most recent differential backup to bring the database forward to the time when that differential backup was created. Typically, you would restore the most recent full backup followed by the most recent differential backup that is based on that full backup.
References: https://technet.microsoft.com/en-us/library/ms345448(v=sql.105).aspx

NEW QUESTION: 3
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Use K-means Clustering to detect faces in the pixels.
Answer: C
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

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