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Three different versions for easy pass.

NEW QUESTION: 1
Azure Machine Learningで機械学習モデルをトレーニングします。ハイパードライブを使用して、ハイパーパラメータを調整します。以前のモデルのトレーニングとチューニングの実行では、多くのモデルが同様のパフォーマンスを示しました。次の要件を満たす早期終了ポリシーを選択する必要があります。
*現在の実行を評価するときに、以前のすべての実行のパフォーマンスを考慮します
*現在の実行とこれまでで最高のパフォーマンスの実行のみを比較するのを避けますそれぞれの正解がソリューションの一部を示しています。
注：それぞれの正しい選択は1ポイントの価値があります。
A. Default
B. Truncation selection
C. Median stopping
D. Bandit
Answer: A

NEW QUESTION: 2
Partitioning can be used to either aid query response time or manage large amounts of InfoCube data.
Which of the following statements are true about InfoCube partitioning? There are 2 correct answers.
A. Logical and table partitioning cannot be combined.
B. The Analytic Engine can do partition pruning.
C. The Repartitioning toolbox can be used with logical partitioning.
D. Table partitions are based either on 0CALMONTH or 0FISCPER.
Answer: B,C,D

NEW QUESTION: 3
승인되지 않은 AWS API 요청을 너무 많이 식별하면 자동 보안 경고를 생성하는 방법은 무엇입니까?
A. CloudTrail 로그 파일에 대해 Amazon Athena SQL 쿼리를 실행하십시오. Amazon QuickSight를 사용하여 운영 대시 보드를 생성하십시오.
B. 이벤트 데이터를 Amazon Kinesis로 스트리밍하도록 AWS CloudTrail을 구성하십시오. 임계 값이 초과되면 경보를 보내도록 스트림에서 AWS Lambda 함수를 구성하십시오.
C. API 호출 오류 코드를 찾는 Amazon CloudWatch 지표 필터를 생성 한 다음 해당 지표의 비율을 기반으로 경보를 구현합니다.
D. Amazon Personal Health Dashboard를 사용하여 계정의 AWS 서비스 사용을 모니터링하고 서비스 오류율이 증가하면 경고를 발생시킵니다.
Answer: B