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NEW QUESTION: 1
Sie haben einen Computer mit dem Namen Computer1, auf dem Windows 10 ausgeführt wird und der die folgenden Dateien enthält:
* C:\Folder1\File1.bat
* C:\Folder1\File1.exe
* C:\Folder1\File1.cmd
Einem Benutzer mit dem Namen Benutzer1 wird Lesen und Ausführen für alle Dateien zugewiesen.
Computer1 ist wie in der Ausstellung gezeigt konfiguriert.
Verwenden Sie die Dropdown-Menüs, um die Antwortauswahl auszuwählen, die die einzelnen Anweisungen anhand der in der Grafik angezeigten Informationen vervollständigt.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Answer:
Explanation:
Erläuterung
Verweise:
https://stackoverflow.com/questions/148968/windows-batch-files-bat-vs-cmd
NEW QUESTION: 2
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:
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: 3
A company is planning to use Amazon S3 lo store images uploaded by its users.
The images must be encrypted at rest in Amazon S3.
The company does not want to spend time managing and rotating the keys, but it does want to control who can access those keys.
What should a solutions architect use to accomplish this?
A. Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS)
B. Server-Side Encryption with keys stored in an S3 bucket
C. Server-Side Encryption with Customer-Provided Keys (SSE-C)
D. Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3)
Answer: D
NEW QUESTION: 4
Which of the following are advantages of configuring a management IP address on a VIO Server?
A. Allows a control channel to be used on SEAsAllows for the use of Live Partition MobilityAllows for security inside a hostile network
B. Allows trunking on the SEA failoverAllows DLPAR of CPU and MemoryAllows network based OS upgrades
C. Allows DLPARAllows network based OS upgradesAllows VIO Server management without access to an HMC
D. Allows the VIO Server to be used as an MSPAllows VIO Server management without access to an HMCAllows browser-based access to ASMI
Answer: C