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NEW QUESTION: 1
ヒューリスティックなSAP_ERP_001を使用しています。計画オーダーが作成されるときに、自動ソース決定ロジックに含まれる基準は次のうちどれですか？
正しい答えを選んでください。
応答：
A. 納期
B. コンポーネントの可用性
C. 費用
D. 割り当ての配置
E. ベンダーの評価
Answer: D

NEW QUESTION: 2
Which adjustments can be made using the Query Builder? There are THREE answers for this Question
A. Copy a query from an SAP-delivered query
B. Create parameters to read values from the user settings
C. Add, remove, or customize fields in queries
D. Add business logic to syntax for queries
E. Create custom Core Data Services (CDS) views for use in queries
Answer: A,C,D

NEW QUESTION: 3
A potential customer requires 800TB of usable capacity to store medical images for their network of health clinics. The IT department has limited staff and currently manages four storage arrays from other vendors. During a meeting with the Director of IT, you learn that the company is planning to deploy a scale-out NAS solution from a competitor of EMC.
Which factors can demonstrate a lower TCO using Isilon?
A. Isilon linear scalability closely aligns with additional staff requirements as capacity grows
B. Isilon uses lower speed NL-SAS drives which reduces overall power and cooling requirements
C. Isilon linear scalability eliminates over buying and over provisioning
D. An IDC study found that Isilon requires 95% less downtime per year compared to other solutions
E. An IDC study found that Isilon requires 95% less downtime hours per year compared to other competitors
F. An IDC study found that Isilon requires 30% less downtime per year compared to other solutions
G. Isilon linear scalability eliminates over buying and over provisioning
H. An IDC study found that Isilon requires 95% less downtime hours per year compared to other competitors
Answer: C,D

NEW QUESTION: 4
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance