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NEW QUESTION: 1
A. Option B
B. Option A
Answer: B
NEW QUESTION: 2
製造会社は飛躍的に成長しており、ITインフラストラクチャとeコマースの存在感を高めるための資金を確保しています。 同社のeコマースプラットフォームは、次のもので構成されています。
*静的アセットは、主にAmazon S3に保存されている製品画像で構成されています。
*商品情報、ユーザー情報、注文情報を格納するAmazon DynamoDBテーブル。
* Elastic Load Balancerの背後にあるアプリケーションのフロントエンドを含むWebサーバー。
同社は、別のリージョンに災害復旧サイトを設置したいと考えています。
ソリューションアーキテクトがすべての要件を満たしながら新しい設計を実装するために実行する必要があるアクションの組み合わせはどれですか。 （3つ選択してください。）
A. Enable Amazon S3 versioning on the source and destination buckets containing static assets to ensure there is a rollback version available in the event of data corruption.
B. Enable Amazon Route 53 health checks to determine if the primary site is down, and route traffic to the disaster recovery site if there is an issue.
C. Enable Amazon CloudWatch and create CloudWatch alarms that route traffic to the disaster recovery site when application latency exceeds the desired threshold.
D. Enable DynamoDB global tables to achieve a multi-Region table replication.
E. Enable Amazon S3 cross-Region replication on the buckets that contain static assets.
F. Enable multi-Region targets on the Elastic Load Balancer and target Amazon EC2 instances in both Regions.
Answer: B,D,E
NEW QUESTION: 3
一連のラベル付き写真を使用するマルチクラス画像分類の深層学習モデルがあります。次のコードを作成して、モデルをトレーニングするときにハイパーパラメーター値を選択します。
次の各ステートメントについて、ステートメントがtrueの場合は[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
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。
Answer:
Explanation:
Explanation
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself.
Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION: 4
A. Option C
B. Option B
C. Option D
D. Option A
Answer: C
Explanation:
Explanation
HTML5 slider contains min and max properties:
* min Minimum value of the range. Default minimum value is 0.
* maxMaximum value of the range. Default maximum value is 100.
Incorrect:
Not A: HTML5 slider contains "range" property, but it used for enabling and configuring range selection in slider, not for setting min and max possible values.
References:
http://www.html5tutorial.info/html5-range.php