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NEW QUESTION: 1
必要に応じて、次のログイン資格情報を使用します。
Azureユーザー名：xxxxx
Azureパスワード：xxxxx
次の情報は、テクニカルサポートのみを目的としています。
ラボインスタンス：10543936

db2という名前のAzure SQLデータベースとdb3という名前の新しいSQLデータベースを含むエラスティックプールを作成する必要があります。
このタスクを完了するには、Azureポータルにサインインします。
Answer:
Explanation:
Step 1: Create a new SQL database named db3
1. Select SQL in the left-hand menu of the Azure portal. If SQL is not in the list, select All services, then type SQL in the search box.
2. Select + Add to open the Select SQL deployment option page. Select Single Database. You can view additional information about the different databases by selecting Show details on the Databases tile.
3. Select Create:

4. Enter the required fields if necessary.
5. Leave the rest of the values as default and select Review + Create at the bottom of the form.
6. Review the final settings and select Create. Use Db3 as database name.
On the SQL Database form, select Create to deploy and provision the resource group, server, and database.
Step 2: Create your elastic pool using the Azure portal.
1. Select Azure SQL in the left-hand menu of the Azure portal. If Azure SQL is not in the list, select All services, then type Azure SQL in the search box.
2. Select + Add to open the Select SQL deployment option page.
3. Select Elastic pool from the Resource type drop-down in the SQL Databases tile. Select Create to create your elastic pool.

4. Configure your elastic pool with the following values:
Name: Provide a unique name for your elastic pool, such as myElasticPool.
Subscription: Select your subscription from the drop-down.
ResourceGroup: Select the resource group.
Server: Select the server

5. Select Configure elastic pool
6. On the Configure page, select the Databases tab, and then choose to Add database.

7. Add the Azure SQL database named db2, and the new SQL database named db3 that you created in Step 1.
8. Select Review + create to review your elastic pool settings and then select Create to create your elastic pool.
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/sql-database/sql-database-elastic-pool-failover-group-tutorial

NEW QUESTION: 2
A nurse calls the physician about an order of a medication that has a high recommended dosage. The
nurse wasn't able to locate the physician and the medication is already due to be administered. The nurse
should:
A. Administer the recommended dose
B. Administer the prescribed dose
C. Contact the nursing supervisor
D. Hold the medication until the physician can be contacted
Answer: C
Explanation:
The nurse should contact the nursing supervisor. If there is no resolution regarding the order
because the physician cannot be located or because the order remains as it was written after talking with
the physician, the nurse then should contact nursing supervisor for further clarification as to what the next
step should be. Under no circumstances should the nurse proceed to carry out the order until obtaining
clarification.

NEW QUESTION: 3
You manage a process that performs analysis of daily web traffic logs on an HDInsight cluster. Each of the
250 web servers generates approximately 10megabytes (MB) of log data each day. All log data is stored in a single folder in Microsoft Azure Data Lake Storage Gen 2.
You need to improve the performance of the process.
Which two changes should you make? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Increase the number of worker nodes
B. Combine the daily log files for all servers into one file
C. Increase the value of the mapreduce map memory parameter
D. Increase the value of the hive.tez.containerize parameter
E. Move the log files into folders so that each day's logs are in their own folder
Answer: B,E
Explanation:
Explanation
A: Typically, analytics engines such as HDInsight and Azure Data Lake Analytics has a per-five overhead. If you store your data as many small files, this can negatively affect performance. In general, organize your data into larger sized files for better performance (256MB to 100GB in size). Some engines and applications might have trouble efficiently processing files that are greater than 100GB in size.
C: For Hive workloads, partition pruning of time-series data can help some queries read only a subset of the data which improves performance.
Those pipelines that ingest time-series data, often place their files with a very structured naming for files and folders. Below is a very common example we see for data is structured by date:
\DataSet\YYYY\MM\DD\datafile_YYYY_MM_DD.tsv
Notice that the datetime information appears both as folders and in the filename.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-performance-tuning-guidance