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NEW QUESTION: 1
オークションのウェブサイトでは、ユーザーは収集可能なアイテムに入札できます。オークションルールでは、各入札は一度だけ、受け取った順序で処理される必要があります。現在の実装は、Amazon Kinesis DataStreamsに入札レコードを書き込むAmazonEC2Webサーバーのフリートに基づいています。単一のt2.largeインスタンスには、Kinesis Data Streamsから着信入札を読み取り、各入札を処理する入札プロセッサを実行するcronジョブがあります。オークションサイトの人気は高まっていますが、一部の入札が登録されていないとユーザーから不満が寄せられています。
トラブルシューティングは、ピーク需要時間中に入札プロセッサが遅すぎ、処理中にクラッシュすることがあり、処理されているレコードを追跡できなくなることがあることを示しています。
入札処理の信頼性を高めるためにどのような変更が必要ですか？
A. Kinesis DataStreamsに入札を投稿するときにAmazonKinesis Producer Library（KPL）を使用するようにWebアプリケーションをリファクタリングします。入札プロセッサをリファクタリングして、Kinesis Data Streamsの各レコードに未読、処理、および処理済みのフラグを付けます。各入札処理の実行の開始時に、Kinesis DataStreamsをスキャンして未処理のレコードを探します。
B. EC2インスタンスタイプをt2.largeからより大きな一般的なコンピューティングインスタンスタイプに切り替えます。 Kinesis Data StreamsのIncomingRecordsメトリックに基づいて、入札プロセッサを実行しているEC2インスタンスの数をスケールアウトするAutoScalingグループに入札プロセッサEC2インスタンスを配置します。
C. Webアプリケーションをリファクタリングして、各着信入札をKinesis DataStreamsの代わりにAmazonSQSFIFOキューに送信します。入札プロセッサをリファクタリングして、SQSキューを継続的に取得します。入札処理EC2インスタンスを、最小サイズと最大サイズが1のAutoScalingグループに配置します。
D. Webアプリケーションをリファクタリングして、Kinesis DataStreamsの代わりにAmazonSNSトピックに各着信入札を投稿します。ユーザーが入札を送信するとすぐに各入札を処理するAWSLambda関数をトリガーするようにSNSトピックを設定します。
Answer: C
Explanation:
FIFO is better in this case compared to Kinesis, as it guarantee the order of the bid. Min Max 1, is okay as the SQS will hold the queue in case of failure of the instance, till it come back again.

NEW QUESTION: 2
For which IMG object can you activate the password self-service (PSS) in Access Control?
A. Condition group
B. Cross system
C. Connector
D. Logical system
Answer: C

NEW QUESTION: 3
You need to build an A) solution that will be shared between several developers and customers.
You plan to write code, host code, and document the runtime all within a single user experience.
You build the environment to host the solution.
Which three actions should you perform in sequence next? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Create an Azure Machine Learning Studio workspace
Step 2: Create a notebook
You can manage notebooks using the UI, the CLI, and by invoking the Workspace API.
To create a notebook
Click the Workspace button Workspace Icon or the Home button Home Icon in the sidebar. Do one of the following:
Next to any folder, click the Menu Dropdown on the right side of the text and select Create > Notebook. Create Notebook In the Workspace or a user folder, click Down Caret and select Create > Notebook.
2. In the Create Notebook dialog, enter a name and select the notebook's primary language.
3. If there are running clusters, the Cluster drop-down displays. Select the cluster to attach the notebook to.
4. Click Create.
Step 3: Create a new experiment
Create a new experiment by clicking +NEW at the bottom of the Machine Learning Studio window. Select EXPERIMENT > Blank Experiment.
References:
https://docs.azuredatabricks.net/user-guide/notebooks/notebook-manage.html
https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-run-cloud-notebook