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NEW QUESTION: 1
A workstation on the network is configured as follows. A user reports that they are unable to print to a device located on a different subnet. Which of the following is the MOST likely problem?
IP address: 172.16.204.12
Subnet Mask: 255.255.0.0
Default Gateway: 172.15.204.254
DNS address: 172.16.204.1
Broadcast address: 172.16.255.255
A. Broadcast address
B. IP address
C. DNS address
D. Subnet mask
E. Default gateway
Answer: E
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
Ein Projekt befindet sich in der Ausführungsphase. Ein Risikomanagementplan wurde implementiert und die Risikoanalyse wird fortgesetzt.
Wie sollen die Auswirkungen identifizierter Risiken dem Stakeholder mitgeteilt werden?
A. Zeichnen Sie eine Wahrscheinlichkeits- und Auswirkungsmatrix oder ein hierarchisches Diagramm, um Risiken zu priorisieren.
B. Nehmen Sie am Opportunity Management der identifizierten Risiken für den Mehrwert teil.
C. Zeigen Sie die Gründe für bevorstehende Risiken entweder anhand eines Affinitätsdiagramms oder eines Matrixdiagramms an.
D. Berechnen Sie die erforderlichen Notfallfinanzierungen und Zeitpuffer, die für die Risikoreaktion erforderlich sind.
Answer: A

NEW QUESTION: 3
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

NEW QUESTION: 4
Amazon Elastic File System（EFS）は、スペースを使用してオブジェクトに使用されるスペースに関する情報を提供します
_ Network File Systemバージョン4.1（NFSv4.1）の属性を使用しました。属性には、メタデータのサイズではなく、オブジェクトの現在の計測データのサイズが含まれます。ファイルで使用されているディスクの量を測定するために、次のユーティリティのうちどれを使用しますか？
A. duユーティリティ
B. blkidユーティリティ
C. sfdiskユーティリティ
D. pydfユーティリティ
Answer: A
Explanation:
Explanation
Amazon EFS reports file system sizes and sizes of objects within a file system. Using the NFSv4.1 space _ used attribute for measuring the space used for an object, it reports only the object's current metered data size and not the metadata size. There are two utilities available for measuring disk usage of a file, the du and stat utilities.
https://docs.aws.amazon.com/efs/latest/ug/metered-sizes.html

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