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NEW QUESTION: 1
Refer to the exhibit.
In the use case displayed in the exhibit, why is Notepad unable to save the changes to this file?
A. SONAR is set to block host file modifications.
B. System Lockdown is enabled.
C. SONAR High Risk detection is set to Block.
D. Tamper Protection is preventing Notepad from modifying the host file.
Answer: A
NEW QUESTION: 2
Which of the following may unilaterally deem a cloud hosting model inappropriate for a system or application?
A. Certification
B. Virtualization
C. Multitenancy
D. Regulation
Answer: D
Explanation:
Some regulations may require specific security controls or certifications be used for hosting certain types of data or functions, and in some circumstances they may be requirements that are unable to be met by any cloud provider.
NEW QUESTION: 3
HOTSPOT
Answer:
Explanation:
NEW QUESTION: 4
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml