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NEW QUESTION: 1
Environment variables can be used to set variables. The environment variables must be in the format "____"_<variablename>. Select the correct prefix string from the following list.
A. TF_ENV
B. TF_VAR_NAME
C. TF_ENV_VAR
D. TF_VAR
Answer: D
Explanation:
Environment variables can be used to set variables. The environment variables must be in the format TF_VAR_name and this will be checked last for a value. For example:
export TF_VAR_region=us-west-1
export TF_VAR_ami=ami-049d8641
export TF_VAR_alist='[1,2,3]'
export TF_VAR_amap='{ foo = "bar", baz = "qux" }'
https://www.terraform.io/docs/commands/environment-variables.html

NEW QUESTION: 2
ある企業には、オンプレミスに保存されたアーカイブされた画像データが150 TBあり、翌月中にAWSクラウドに刈り込む必要があります。同社の現在のネットワーク接続では、この目的で夜間のみ最大100 Mbpsのアップロードが可能です。
このデータを移動して移行期限を守るための最も費用対効果の高いメカニズムは何ですか？
A. Amazon S3 VPCエンドポイントを作成し、VPNを確立してデータをアップロードします。
B. AWS Snowmobileを使用してデータをAWSに送信します。
C. データをAWSに発送するために複数のAWS Snowballデバイスを注文します。
D. Amazon S3 Transfer Accelerationを有効にして、データを安全にアップロードします。
Answer: C

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 K-means Clustering to detect faces in the pixels.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Use feature engineering to add features for eyes, noses, and mouths to the input data.
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

NEW QUESTION: 4
Which of the following is used to view system resource assignment when a piece of hardware is not working?
A. Event Viewer
B. Device Driver
C. Task Manager
D. Device Manager
Answer: D