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NEW QUESTION: 1
Ein Unternehmen plant die Migration von einem Microsoft-Volumenlizenzmodell auf ein abonnementbasiertes Modell.
Aktualisierungen von Geräten müssen die folgenden Anforderungen erfüllen:

Sie müssen die entsprechenden Wartungsmodelle empfehlen, um Mitarbeitergeräte zu aktualisieren.
Um zu antworten, ziehen Sie das Servicemodell aus der linken Spalte auf die rechte Komponente. Jedes Modell kann einmal, mehrmals oder gar nicht verwendet werden.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.

Answer:
Explanation:

Explanation:
References:
https://docs.microsoft.com/en-us/windows/deployment/update/waas-overview#servicing-tools

NEW QUESTION: 2
会社がAWS CodeBuildを使用してコンテナベースのアプリケーションをデプロイしています。セキュリティチームは、パスワードで保護されたエンドポイントを使用して展開する前に、すべてのコンテナの脆弱性をスキャンすることを義務付けています。すべての機密情報は安全に保管する必要があります。
これらの要件を満たすには、どのソリューションを使用する必要がありますか？
A. AWS Encryption SDKを使用してパスワードを暗号化し、シークレットマッピングの下の変数としてbuildspec.ymlファイルに埋め込みます。 CodeBuildにポリシーをアタッチして、必要な復号化キーへのアクセスを有効にします。
B. パスワードを安全な文字列としてAWS Systems Managerパラメータストアに保存します。パラメーターストアマッピングの下で​​、パラメーターストアキーを環境変数としてbuildspec.ymlファイルに追加します。環境変数を参照してスキャンを開始します。
C. AWS KMSを使用してパスワードを暗号化します。暗号化されたパスワードを変数マッピングの下の環境変数としてbuildspec.ymlファイルに保存します。環境変数を参照してスキャンを開始します。
D. パスワードをAWS CloudHSMキーにインポートします。変数マッピングで環境変数としてbuildpec.ymlファイルのCloudHSMキーを参照します。環境変数を参照してスキャンを開始します。
Answer: B

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
* A workload for data engineers who will use Python and SQL
* A workload for jobs that will run notebooks that use Python, Spark, Scala, and SQL
* A workload that data scientists will use to perform ad hoc analysis in Scala and R The enterprise architecture team at your company identifies the following standards for Databricks environments:
* The data engineers must share a cluster.
* The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
* All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databrick clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
We would need a High Concurrency cluster for the jobs.
Note:
Standard clusters are recommended for a single user. Standard can run workloads developed in any language:
Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
References:
https://docs.azuredatabricks.net/clusters/configure.html