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NEW QUESTION: 1
A manufacturer experiences frequent changes in product technology and market preferences, resulting in new product introductions each year. Which of the following sourcing strategies would be most appropriate?
A. Sole sourcing
B. Long-term contracting
C. Multiple sourcing
D. Strategic partnering
Answer: D
NEW QUESTION: 2
Azure Resources Managerテンプレートを使用してAzureサービスを展開します。テンプレート参照の秘密はAzureキーVaultに格納されています。
展開中に秘密にアクセスするためのソリューションを推奨する必要があります。このソリューションでは、展開を実行しているユーザーがKey Vault内の秘密に直接アクセスするのを防ぐ必要があります。
あなたは推薦に何を含めるべきですか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれ正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: An advanced access policy for the key vaults
Enable template deployment
* In the portal, select your Key Vault.
* Select Access policies.
* Select access policies
* Select Click to show advanced access policies.
* Show advanced access policies
* Select Enable access to Azure Resource Manager for template deployment. Then, select Save.
Box 2: Role-based access control (RBAC)
In large teams you may have multiple people deploying resources but don't want to give them access to the actual secrets inside the vault. You can achieve this by creating a custom role that only gives access to the KeyVault for deployment purposes. The deployment user cannot read the secrets within.
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/managed-applications/key-vault-access
https://azurecto.com/azure-key-vault-custom-role-for-deployment/
NEW QUESTION: 3
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.
A. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
B. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
D. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
Answer: A,C