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NEW QUESTION: 1
Which of the following BEST helps to ensure system resiliency for a business application that processes high-volume and real-lime retail transactions?
A. Adequate reciprocal recovery agreements
B. Adequate resourcing for the incident response team
C. Adequate application clustering
D. Adequate encryption between applications
Answer: C

NEW QUESTION: 2
You want to create a guaranteed restore point for your database by executing the command:
SQL> CREATE RESTORE POINT dbrsp1 GUARANTEE FLASHBACK DATABASE;
Identify two prerequisites for the successful execution of this command.
A. Undo retention guarantee must be enabled.
B. A database backup must be taken.
C. The recyclebin must be enabled for the database.
D. Flashback Database must be enabled.
E. The database must be running in archivelog mode.
F. Fast Recovery Area must be enabled.
Answer: E,F
Explanation:
Explanation/Reference:
Explanation:
SQL> select flashback_on from v$database;
FLASHBACK_ON
------
NO
SQL> CREATE RESTORE POINT dbrsp1 GUARANTEE FLASHBACK DATABASE;
Restore point created.
SQL> archive log list;
Database log mode Archive Mode
Automatic archival Enabled
Archive destination USE_DB_RECOVERY_FILE_DEST
Oldest online log sequence 113
Next log sequence to archive 115
Current log sequence 115
SQL>
http://docs.oracle.com/cd/B19306_01/backup.102/b14192/rpfbdb002.htm

NEW QUESTION: 3
CPUベースのコンピューティングクラスターとAzureKubernetes Services（AKS）推論クラスターを含むAzure MachineLearningワークスペースがあります。分類モデルの作成に使用する予定のデータを含む表形式のデータセットを作成します。
Azure Machine Learningデザイナーを使用して、クライアントアプリケーションが新しいデータを送信し、応答として即時予測を取得することで分類モデルを利用できるWebサービスを作成する必要があります。
どの3つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序で配置します。

Answer:
Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/