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NEW QUESTION: 1
チームには、Amazon S3バケットにアップロードされる新しいオブジェクトを検出するアプリケーションがあります。
アップロードはLambda関数をトリガーし、オブジェクトメタデータをAmazon DynamoDBテーブルとRDS PostgreSQLデータベースに書き込みます。
高可用性を確保するには、チームはどのアクションを実行する必要がありますか？
A. Amazon S3バケットでクロスリージョンレプリケーションを有効にします。
B. DynamoDBテーブルのDynamoDBストリームを作成します。
C. RDS PostgreSQLデータベースでマルチAZを有効にします。
D. アプリケーションがデプロイされる各アベイラビリティーゾーンにLambda関数を作成します。
Answer: C
Explanation:
Explanation
"Amazon RDS provides high availability and failover support for DB instances using Multi-AZ deployments"
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.MultiAZ.html

NEW QUESTION: 2
The manager of a call center reports that staffing the center is difficult because the number of calls is unpredictable.
You have historical data that contains information about the calls.
You need to build an Azure Machine Learning experiment to predict the number of total calls each hour.
Which model should you use?
A. Multiclass Logistic Regression
B. Boosted Decision Tree Regression
C. Poisson Regression
D. Decision Forest Regression
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
The response variable has a Poisson distribution.
Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non- whole numbers.
Incorrect Answers:
A: Logistic regression is a well-known method in statistics that is used to predict the probability of an outcome, and is particularly popular for classification tasks. The algorithm predicts the probability of occurrence of an event by fitting data to a logistic function.
B: Boosting means that each tree is dependent on prior trees. The algorithm learns by fitting the residual of the trees that preceded it. Thus, boosting in a decision tree ensemble tends to improve accuracy with some small risk of less coverage.
C: Decision trees are non-parametric models that perform a sequence of simple tests for each instance, traversing a binary tree data structure until a leaf node (decision) is reached.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson- regression

NEW QUESTION: 3
Siehe Ausstellung.

Wenn OSPF in diesem Netzwerk ausgeführt wird, wie verarbeitet Router 2 den Datenverkehr von Standort B bis 10.10.13 / 25 an Standort A?
A. Es werden nur Pakete über die Schnittstelle Fa0 / 2 gesendet.
B. Es gleicht den Verkehr aus Fa0 / 1 und Fa0 / 2 aus
C. Es werden nur Pakete über die Schnittstelle Fa0 / 1 gesendet.
D. Es können keine Pakete an 10.10.13 128/25 gesendet werden
Answer: D
Explanation:
Explanation
Router2 does not have an entry for the subnet 10.10.13.128/25. It only has an entry for 10.10.13.0/25, which ranges from 10.10.13.0 to 10.10.13.127.