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NEW QUESTION: 1
A. Option C
B. Option B
C. Option D
D. Option A
Answer: D
NEW QUESTION: 2
企業は、サーバーレスインフラストラクチャで画像処理アプリケーションを実行しています。各処理ジョブは、1回のAWSLambda実行で実行されます。シスオペ管理者は、他のLambda関数が他のアプリケーションに対して実行されている場合でも、500の同時ジョブを実行するのに十分な容量があることを確認する必要があります。管理者は、リージョン内のサービス制限をすでに引き上げています。
どのアクションを実行する必要がありますか？
A. Lambda関数のメモリ設定を変更して、500回の並列実行を許可します
B. 画像処理ロジックをAWSステップ関数に移動します
C. スロットルされた実行を再試行するように配信不能キューを構成します
D. 画像処理Lambda関数の予約済み同時実行性を500に設定します
Answer: D
Explanation:
https://docs.aws.amazon.com/lambda/latest/dg/invocation-scaling.html
NEW QUESTION: 3
기계 학습 전문가는 대기업과 협력하여 제품 내에서 기계 학습을 활용합니다. 이 회사는 향후 6 개월 이내에 고객이 이탈 할 수 있는 고객을 기준으로 고객을 카테고리로 그룹화하려고 합니다. 회사는 전문가가 사용할 수 있는 데이터를 표시했습니다.
전문가가 이 작업을 수행하기 위해 어떤 기계 학습 모델 유형을 사용해야 합니까?
A. 강화 학습
B. 클러스터링
C. 선형 회귀
D. 분류
Answer: D
Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) ?answers that need to be predicted ?to train an algorithm.
With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html