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NEW QUESTION: 1
If your customer is looking at coping with evolving threats and how costly a breach could be. which customer trend would you refer to?
A. insourcing versus outsourcing
B. focus on security
C. environmental impact
D. fleet predictability and transparency
Answer: A
NEW QUESTION: 2
A. Option D
B. Option B
C. Option C
D. Option F
E. Option A
F. Option E
Answer: A,B,D
NEW QUESTION: 3
Welcher der folgenden Schritte ist der wichtigste bei der Entwicklung eines effektiven IT-Governance-Aktionsplans?
A. Messung der IT-Governance-Leistungsindikatoren (KPIs)
B. Vorbereitung einer Sensitivitätserklärung
C. Durchführung einer Business Impact Analysis (BIA)
D. Einrichten eines IT-Governance-Frameworks für den Prozess
Answer: D
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
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