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NEW QUESTION: 1
PyTorch深層学習フレームワークを使用するマルチクラス画像分類深層学習モデルを作成します。
分類モデルのハイパーパラメーターを最適化するには、Azure Machine Learningハイパードライブを構成する必要があります。
精度が最も高いモデルとなるハイパーパラメーター値を決定するには、プライマリメトリックを定義する必要があります。
どの3つのアクションを実行する必要がありますか？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. 最小化するためにbird_classifier_train.pyスクリプトを実行するために使用される推定器のprimary_metric_goalを設定します。
B. bird_classifier_train.pyスクリプトの実行に使用される推定器のprimary_metric_nameをlossに設定します。
C. bird_classifier_train.pyスクリプトを実行するために使用される推定器のprimary_metric_nameを正確に設定します。
D. bird_classifier_train.pyスクリプトにコードを追加して、モデルの検証損失を計算し、キー損失のある浮動小数点値としてログに記録します。
E. bird_classifier_train.pyスクリプトにコードを追加して、モデルの検証精度を計算し、それをキー精度の浮動小数点値として記録します。
F. bird_classifier_train.pyスクリプトを実行して最大化するために使用される推定器のprimary_metric_goalを設定します。
Answer: C,E,F
Explanation:
Explanation
AD:
primary_metric_name="accuracy",
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE
Optimize the runs to maximize "accuracy". Make sure to log this value in your training script.
Note:
primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script.
primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.
F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.

NEW QUESTION: 2


While using the Microsoft Assessment and planning toolkit, please answer the following question.
How many computers were identified as candidates to be virtualized on Hyper-V?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: E

NEW QUESTION: 3
부서는 회사의 로그 파일에서 정기적 인 분석 보고서를 생성합니다. 모든 로그 데이터는 Amazon S3에서 수집되어 Amazon Redshift 데이터웨어 하우스에 대해 매일 PDF 보고서와 CSV 형식으로 집계 된 테이블을 생성하는 매일 Amazon Elastic MapReduce (EMR) 작업으로 처리됩니다.
CFO는이 시스템의 비용 구조를 최적화하도록 요청합니다.
다음 대안 중 어느 것이 시스템의 평균 성능이나 원시 데이터의 데이터 무결성을 손상시키지 않으면 서 비용을 절감 할 수 있습니까?
A. Amazon S3에서 PDF 및 .csv 데이터에 RRS (Redundancy Storage)를 사용합니다. 스팟 인스턴스를 Amazon EMR 작업에 추가하십시오. Amazon Redshift에 예약 인스턴스를 사용하십시오.
B. S3에서 PDF 및 .csv 데이터에 대해 중복 중복 저장소 (RRS)를 사용하십시오. 스팟 인스턴스를 EMR 작업에 추가하십시오.
Amazon Redshift에 스팟 인스턴스를 사용하십시오.
C. Amazon S3의 모든 데이터에 대해 중복 중복 스토리지 (RRS)를 사용합니다. 스팟 인스턴스를 Amazon EMR 작업에 추가하십시오. Amazon Redshift에 예약 인스턴스를 사용하십시오.
D. S3의 모든 데이터에 대해 축소 중복 저장소 (RRS)를 사용하십시오. Amazon EMR 작업에 스팟 인스턴스와 예약 인스턴스를 함께 사용하십시오. Amazon Redshift에 예약 인스턴스를 사용하십시오.
Answer: A
Explanation:
설명
중복 중복 스토리지 사용 Amazon S3는 스토리지 클래스에 따라 객체를 저장합니다. Amazon S3에 쓸 때 스토리지 클래스를 객체에 할당합니다. 객체를 Amazon S3 버킷에 쓰거나 Amazon S3에 이미 저장된 객체를 복사 할 때만 객체에 특정 스토리지 클래스 (표준 또는 중복 중복)를 할당 할 수 있습니다. 표준이 기본 스토리지 클래스입니다. 스토리지 클래스에 대한 정보는 오브젝트 키 및 메타 데이터를 참조하십시오.
스토리지 비용을 절감하기 위해 Amazon S3가 표준 스토리지로 제공하는 것보다 낮은 수준의 중복성에서 중요하지 않은 재현 가능한 데이터에 대해 중복성 감소 스토리지를 사용할 수 있습니다. 중복 수준이 낮을수록 내구성과 가용성이 낮아 지지만 대부분의 경우 비용이 낮을수록 중복 저장소를 수용 가능한 스토리지 솔루션으로 만들 수 있습니다. 예를 들어, 다른 곳에 지속적으로 저장되는 미디어 컨텐츠를 공유하기위한 비용 효율적인 솔루션 일 수 있습니다. 원본 이미지에서 쉽게 재생할 수있는 축소판 및 기타 크기 조정 된 이미지를 저장하는 경우에도 의미가 있습니다.
중복 스토리지 감소는 특정 연도에 99.99 %의 내구성을 제공하도록 설계되었습니다. 이 내구성 수준은 평균 연간 예상 손실률 0.01 %에 해당합니다. 예를 들어
RRS 옵션을 사용하여 10,000 개의 개체를 사용하면 평균적으로 연간 단일 개체 (연간 10,000 개 개체의 0.01 %)가 매년 손실 될 것으로 예상 할 수 있습니다.
노트 :
이 연간 손실은 예상 평균을 나타내며 주어진 연도에 0.01 % 미만의 객체 손실을 보장하지 않습니다.
중복성 감소 스토리지는 여러 시설의 여러 디바이스에 객체를 저장하여 일반적인 디스크 드라이브보다 400 배의 내구성을 제공하지만 Amazon S3 표준 스토리지만큼 여러 번 객체를 복제하지는 않습니다. 또한 중복 스토리지 감소는 단일 시설에서 데이터 손실을 유지하도록 설계되었습니다.
중복 스토리지 감소의 객체가 손실 된 경우 Amazon S3는 해당 객체에 대한 요청에서 405 오류를 반환합니다. Amazon S3는 중복 스토리지 객체 손실 감소에 대한 알림도 제공합니다. Amazon S3가 RRS 객체의 손실을 감지하면 Amazon Simple Notification Service (Amazon SNS)를 통해 알림이 전송되도록 버킷을 구성 할 수 있습니다. 그런 다음 손실 된 개체를 교체 할 수 있습니다. 알림을 활성화하려면 Amazon S3 콘솔을 사용하여 버킷의 알림 속성을 설정할 수 있습니다.

NEW QUESTION: 4
Which of the following statements represents the correct definition of the terms 'cost allocation' and 'chargeback'?
A. 'Cost allocation' relates to identifying costs and assigning them to specific cost categories. 'Chargeback' is focused on recovering costs from consumers of the cloud-delivered services.
B. 'Cost allocation' and 'chargeback' are synonymous terms that relate to allocating costs of cloud services and ensuring that billing occurs for the services being delivered.
C. 'Cost allocation' and 'chargeback' relate to ensuring that cloud computing delivery services are a profit center instead of a cost center. This leads to a positive return on investment for both cloud provider and cloud consumer.
D. 'Cost allocation' focuses on recovering costs from consumers of cloud-delivered services whereas 'chargeback' relates to identifying costs and assigning them to specific cost categories.
Answer: A