Based on the classification tree shown below, using the pairwise testing technique how many test cases are required to test all pairs?
A. 0
B. 1
C. 2
D. 3
Answer: B
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NEW QUESTION: 1
Based on the classification tree shown below, using the pairwise testing technique how many test cases are required to test all pairs?
A. 0
B. 1
C. 2
D. 3
Answer: B
NEW QUESTION: 2
クラウドコンピューティングの多くの側面は、従来のデータセンターよりも大きなメリットをもたらしますが、クラウドコンピューティングに固有の新しい課題ももたらします。
クラウドコンピューティングの次の側面のうち、重要度の高い適切なデータ分類を行うものはどれですか。
A. 相互運用性
B. 移植性
C. マルチテナンシー
D. 可逆性
Answer: C
Explanation:
With multitenancy, where different cloud customers all share the same physical systems and networks, data classification becomes even more important to ensure that the appropriate security controls are applied immediately to prevent any potential leakage or exposure to other customers. Portability refers to the ability to move easily from one cloud provider to another.
Interoperability refers to the ability to reuse components and services for different uses.
Reversibility refers to the ability of the cloud customer to quickly and completely remove all data and services from a cloud provider and to verify the removal.
NEW QUESTION: 3
Health Insightsデータプラットフォーム用のストレージを設計する必要があります。
どのタイプのテーブルを設計に含める必要がありますか？ 回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Hash-distributed tables
The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
Hash-distributed tables improve query performance on large fact tables.
Box 2: Round-robin distributed tables
A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random.
Scenario:
ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute