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NEW QUESTION: 1
A web-startup runs its very successful social news application on Amazon EC2 with an Elastic Load Balancer, an Auto-Scaling group of Java/Tomcat application-servers, and DynamoDB as data store. The main web-application best runs on m2 x large instances since it is highly memory- bound Each new deployment requires semi-automated creation and testing of a new AMI for the application servers which takes quite a while ana is therefore only done once per week.
Recently, a new chat feature has been implemented in nodejs and wails to be integrated in the architecture. First tests show that the new component is CPU bound Because the company has some experience with using Chef, they decided to streamline the deployment process and use AWS Ops Works as an application life cycle tool to simplify management of the application and reduce the deployment cycles.
What configuration in AWS Ops Works is necessary to integrate the new chat module in the most cost-efficient and flexible way?
A. Create one AWS Ops Works stack create two AWS Ops Works layers create one custom recipe
B. Create one AWS Ops Works stack, create one AWS Ops Works layer, create one custom recipe
C. Create two AWS Ops Works stacks create two AWS Ops Works layers create two custom recipe
D. Create two AWS Ops Works stacks create two AWS Ops Works layers create one custom recipe
Answer: D
NEW QUESTION: 2
A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:
At the end of each month, a new folder with that month's sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
* You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
* You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
* You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?
A. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
B. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.
C. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the
D. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
Answer: A
Explanation:
Explanation
Specify the path.
Example:
The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.
from azureml.core import Workspace, Datastore, Dataset
datastore_name = 'your datastore name'
# get existing workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name
datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore
datastore_paths = [(datastore, 'weather/2018/11.csv'),
(datastore, 'weather/2018/12.csv'),
(datastore, 'weather/2019/*.csv')]
weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)
NEW QUESTION: 3
When you are administrating Veritas Volume Manager on the Solaris platform, you want to remove a disk using vxdiskunsetup command from the Veritas Volume Manager without erasing the VxVM metadata on the disk.
What is the necessary step you need to do before running the command in the scenario and to prevent a data loss?
A. Disable a disk group before you remove a disk.
B. Data on the disk should be evacuated from the disk.
C. Move data using Veritas Volume Manager.
D. You need to encapsulate the disk.
Answer: B
NEW QUESTION: 4
multiplatfotm、multiple-siteクライアント/サーバー環境でのアクセス制御の最も難しい側面は何ですか？
A. すべてのプラットフォームで一貫性を維持
B. ローカルユーザーをホストサーバー上の必要なリソースに制限する
C. ローカルユーザーをローカルプラットフォーム上の必要なリソースに制限する
D. 少数のホストでのみ有効な新しいユーザーIDの作成
Answer: A