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NEW QUESTION: 1

A. Option D
B. Option B
C. Option C
D. Option E
E. Option A
Answer: A,C,D

NEW QUESTION: 2
言い直し（第2）法の抵触は、次のうちどれを指しますか？
A. 裁判官が意見で法律を言い換えるとき
B. 管轄紛争の解決方法
C. 相反する法律が存在する状況でどの法律が最も適切であるかを決定するための基礎
D. ある状況で地方法または連邦法が適用されるかどうか
Answer: C

NEW QUESTION: 3
パッシブOSフィンガープリンティングに使用できるツールは次のうちどれですか？
A. tcpdump
B. ping
C. tracert
D. nmap
Answer: A

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a data warehouse that stores sales data. One fact table has 100 million rows.
You must reduce storage needs for the data warehouse.
You need to implement a solution that uses column-based storage and provides real-time analytics for the operational workload.
Solution: You remove any clustered indexes and load the table for processing.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Clustered columnstore tables offer both the highest level of data compression as well as the best overall query performance. Clustered columnstore tables will generally outperform clustered index or heap tables and are usually the best choice for large tables. For these reasons, clustered columnstore is the best place to start when you are unsure of how to index your table.
Note: Dimensional tables can be used to reduce the size of fact tables.
Dimension tables contain attribute data that might change but usually changes infrequently.
For example, a customer's name and address are stored in a dimension table and updated only when the customer's profile changes. To minimize the size of a large fact table, the customer's name and address do not need to be in every row of a fact table. Instead, the fact table and the dimension table can share a customer ID. A query can join the two tables to associate a customer's profile and transactions.
References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data- warehouse-tables-overview