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NEW QUESTION: 1
100万行を超える大規模なデータセットがあります。このテーブルには、Dateという名前の日時列があります。
データモデルのサイズを縮小する必要があります。
あなたは何をするべきか?
A. 日付列をトリミングします。
B. 日付列を2つの列に分割します。1つは時刻のみを含み、もう1つは日付のみを含みます。
C. 日付列のデータ型をテキストに変更します。
D. 日付列の時間をstartOfHourに丸めます。
Answer: B
Explanation:
We have to separate date & time tables. Also, we don't need to put the time into the date table, because the time is repeated every day.
Split your DateTime column into a separate date & time columns in fact table, so that you can join the date to the date table & the time to the time table. The time need to be converted to the nearest round minute or second so that every time in your data corresponds to a row in your time table.
Reference:
https://intellipaat.com/community/6461/how-to-include-time-in-date-hierarchy-in-power-bi
NEW QUESTION: 2
A penetration tester is preparing for an audit of critical systems that may impact the security of the environment. This includes the external perimeter and the internal perimeter of the environment. During which of the following processes is this type of information normally gathered?
A. Authorization
B. Enumeration
C. Timing
D. Scoping
Answer: D
NEW QUESTION: 3
A. Option B
B. Option C
C. Option A
D. Option D
Answer: C
Explanation:
Example:
Task:Display a list of recovery plan files that were created for this server using the specified
device class.
Command query rpfile devclass=* format=detailed Output
Recovery Plan File Name: ALASKA.20000406.170423 Node Name: BRANCH1 Device Class Name: REMOTE Recovery Plan File Type: RPFILE Mgmt Class Name: STANDARD Recovery Plan File Size: 16,255 Bytes Marked for Deletion: Yes Deletion Date: 06/12/2000 13:05:31
Recovery Plan File Name: ALASKA.20000407.170845 Node Name: BRANCH1 Device Class Name: REMOTE Recovery Plan File Type: RPFSNAPSHOT Mgmt Class Name: STANDARD Recovery Plan File Size: 16,425 Bytes Marked for Deletion: No Deletion Date:
NEW QUESTION: 4
AWS Data Pipeline의 일정 파이프 라인과 관련된 세 가지 유형의 항목 중 하나 인 시도는 강력한 데이터 관리 기능을 제공합니다.
다음 중 시도에 대해 진실이 아닌 것은 무엇입니까?
A. AWS Data Pipeline Attempt 객체는 적용 가능한 경우 다양한 시도, 결과 및 실패 이유를 추적합니다.
B. AWS Data Pipeline은 재시도 횟수가 허용 된 최대 재시도 횟수에 도달 할 때까지 실패한 작업을 재 시도합니다.
C. 강력한 데이터 관리를 시도합니다.
D. AWS Data Pipeline Attempt 객체는 파이프 라인 구성 요소를 컴파일하여 실행 가능한 인스턴스 세트를 생성합니다.
Answer: D
Explanation:
Attempts, one of the three types of items associated with a schedule pipeline in AWS Data Pipeline, provides robust data management. AWS Data Pipeline retries a failed operation. It continues to do so until the task reaches the maximum number of allowed retry attempts. Attempt objects track the various attempts, results, and failure reasons if applicable. Essentially, it is the instance with a counter. AWS Data Pipeline performs retries using the same resources from the previous attempts, such as Amazon EMR clusters and EC2 instances.
http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-how-tasks-scheduled.html
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