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NEW QUESTION: 1
Which of the following is considered an optical storage medium?
A. Blu-Ray
B. Flash drive
C. SSD
D. Memory card
Answer: A

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
ロジスティック回帰アルゴリズムを使用して分類モデルをトレーニングします。
各機能の重要度を、全体的なグローバル相対重要度値として、および特定の予測セットのローカル重要度の尺度として計算することにより、モデルの予測を説明できる必要があります。
必要なグローバルおよびローカルの特徴重要度値を取得するために使用できる説明を作成する必要があります。
解決策：MimicExplainerを作成します。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.
Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

NEW QUESTION: 3
한 회사가 300 개 이상의 글로벌 웹 사이트와 애플리케이션을 호스팅합니다. 이 회사는 매일 30TB 이상의 클릭 스트림 데이터를 분석 할 수 있는 플랫폼이 필요합니다. 솔루션 아키텍트는 클릭 스트림 데이터를 전송하고 처리하기 위해 무엇을 해야 합니까?
A. Amazon EC2 인스턴스의 Auto Scaling 그룹을 생성하여 데이터를 처리하고 Amazon Redshift가 분석에 사용할 수 있도록 Amazon S3 데이터 레이크로 보냅니다.
B. Amazon Kinesis Data Streams에서 데이터를 수집합니다. Amazon Kinesis Data firehose를 사용하여 데이터를 Amazon S3 데이터 레이크로 전송합니다. 분석을 위해 Amazon Redshift에 데이터를 로드합니다.
C. Amazon CloudFront에 데이터를 캐시합니다. Amazon S3 버킷에 데이터를 저장합니다. 객체가 S3 버킷에 추가되면 AWS Lambda 함수를 실행하여 분석을 위해 데이터를 처리합니다.
D. 데이터를 Amazon S3 버킷에 보관하고 분석을 생성하기 위해 데이터로 Amazon EMR 클러스터를 실행하도록 AWS Data Pipeline을 설계합니다.
Answer: C