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NEW QUESTION: 1
n層アプリケーションでは、Amazon RDS MySQL DBインスタンスのテーブルを削除して、デプロイごとに再入力する必要があります。このプロセスには数分かかる場合があり、プロセスが完了するまでWeb層はオンラインになりません。現在、Web層はAmazon EC2 Auto Scalingグループで構成されており、インスタンスは各デプロイメントで終了および置換されます。 MySQLテーブルは、AWS CodeBuildジョブを介してSQLクエリを実行することで入力されます。
データベースが完全に構成される前にWeb層がオンラインにならないようにするために何をすべきですか？
A. AWS Step Functionsを使用して、データ入力の状態を監視および維持します。展開を続行する前に、データベースを稼働中としてマークします。
B. Auto Scalingグループの起動設定を変更して、ユーザーデータの実行を600秒間一時停止し、テーブルにデータを入力できるようにします。
C. Amazon AuroraをRDS MySQLのドロップイン代替として使用します。スナップショットを使用して、テーブルに正しいデータを入力します。
D. EC2 Auto Scalingライフサイクルフックを使用して、テーブルが入力されるまでWeb層の構成を一時停止します。
Answer: B
NEW QUESTION: 2
A. Option A
B. Option D
C. Option C
D. Option B
Answer: B
Explanation:
IBM Tivoli Storage FlashCopy Manager uses the copy services capabilities ofintelligent disk subsystems to create point-in-time copies. These are application aware copies (FlashCopyor snapshot) of the production data. This copy is then retained on disk as backup allowing for a fast restore operation (Flashback).
Reference:Storage FlashCopy Manager,Installation and User's Guide for AIX
NEW QUESTION: 3
소매 업체에서 기계 학습을 사용하여 신제품을 분류하려고 합니다. 현재 제품의 레이블이 지정된 데이터 세트가 데이터 과학 팀에 제공되었습니다. 데이터 세트에 1,200 개의 제품이 포함되어 있습니다.
제목 크기, 무게 및 가격과 같은 각 제품에 대한 15 가지 기능 각 제품에는 서적, 게임, 전자 제품 및 영화와 같은 6 가지 범주 중 하나에 속하는 것으로 표시되어 있습니다.
교육을 위해 제공된 데이터 세트를 사용하여 신제품을 분류하기 위해 어떤 모델을 사용해야 합니까?
A. 마지막 계층에 대해 softmax 활성화 기능이 있는 심층 컨볼 루션 신경망 (CNN)
B. RNN (Recurrent Neural Network) 기반의 DeepAR 예측 모델
C. 트리 수가 제품 범주 수와 동일하게 설정된 회귀 포리스트
D. objective 매개 변수가 multi로 설정된 XGBoost 모델 : softmax
Answer: D
Explanation:
A XGBoost multi class classification.
https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-
66195e4d9f2d
CNN is used for image classificaiton problems.
