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NEW QUESTION: 1
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 High Concurrency cluster for the jobs.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
We need a High Concurrency cluster for the data engineers and 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
NEW QUESTION: 2
Which of the following does not apply to synthetic narcotics?
A. Two of the most widely available synthetic narcotics are meperidine and methadone.
B. Synthetic narcotics are produced entirely within the laboratory.
C. Meperidine can be administered by injection or taken orally.
D. Large doses can result in convulsions.
E. None of the above.
F. Synthetic narcotics are also covered by the Controlled Substances Act.
Answer: E
NEW QUESTION: 3
クリックして各目的を展開します。 Azureポータルに接続するには、ブラウザーのアドレスバーにhttps://portal.azure.comと入力します。
すべてのタスクの実行が終了したら、「次へ」ボタンをクリックします。
[次へ]ボタンをクリックすると、ラボに戻ることができないことに注意してください。採点は、残りの試験を完了する間、バックグラウンドで発生します。
概要
試験の次のセクションはラボです。このセクションでは、ライブ環境で一連のタスクを実行します。ほとんどの機能はライブ環境と同様に使用できますが、一部の機能(コピーと貼り付け、外部Webサイトへの移動機能など)は設計上不可能です。
スコアは、ラボで述べられているタスクを実行した結果に基づいています。言い換えれば、タスクをどのように達成するかは問題ではありません。タスクを正常に実行すると、そのタスクのクレジットを獲得できます。
ラボの時間は個別ではなく、この試験には複数のラボが必要な場合があります。各ラボを完了するのに必要な時間を使用できます。ただし、与えられた時間内にラボと試験の他のすべてのセクションを完了することができるように、時間を適切に管理する必要があります。
ラボ内で[次へ]ボタンをクリックして作業を送信すると、ラボに戻ることはできません。
ラボを開始するには
[次へ]ボタンをクリックして、ラボを開始できます。
Ubuntu Serverイメージに基づいてVM1004aという名前のAzure仮想マシンを展開し、次の要件を満たすようにVM1004aを構成する必要があります。
-仮想マシンには、少なくとも15 TBのデータを保存できるデータディスクが含まれている必要があります
-データディスクは、少なくとも2,000 IOPSを提供できる必要があります。
-ストレージコストを最小限に抑える必要があります
Azureポータルから何をすべきですか?
Answer:
Explanation:
以下の解決策を参照してください。
説明
ステップ1:Azureポータルを開きます。
ステップ2:左側のメニューで、[すべてのリソース]を選択します。タイプ別にリソースを並べ替えて、画像を簡単に見つけることができます。
ステップ3:リストから使用する画像を選択します。画像ページが開きます。
ステップ4:メニューから[VMの作成]を選択します。
ステップ5:仮想マシン情報を入力します。最初の仮想マシンの名前としてVM1004aを選択します。ここで入力したユーザー名とパスワードは、仮想マシンへのログインに使用されます。完了したら、[OK]を選択します。
既存のリソースグループに新しいVMを作成するか、[新規作成]を選択してVMを保存する新しいリソースグループを作成できます。
ステップ6:VMのサイズを選択します。さらにサイズを表示するには、[すべて表示]を選択するか、サポートされているディスクタイプフィルターを変更します。15TBのデータをサポートするには、プレミアムディスクが必要です。
ステップ7:[設定]で、必要に応じて変更を加え、[OK]を選択します。
ステップ8:概要ページで、画像名がプライベート画像として一覧表示されているはずです。 [OK]を選択して、仮想マシンの展開を開始します。
参照:
https://docs.microsoft.com/en-us/azure/virtual-machines/windows/create-vm-generalized-managed