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NEW QUESTION: 1

A. Option B
B. Option C
C. Option A
D. Option D
Answer: C

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 explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name sales_dataset 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 month tag as necessary.
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 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.
D. 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.
Answer: D
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
2つのテーブルの構造を調べます。

どの2つのクエリが正常に実行されますか？ （2つ選択してください。）

A. オプションE
B. オプションD
C. オプションB
D. オプションA
E. オプションC
Answer: B,E

NEW QUESTION: 4
A company is developing a highly resilient application to be hosted on multiple Amazon EC2 instances . The application will store highly sensitive user data in Amazon RDS tables The application must
* Include migration to a different AWS Region in the application disaster recovery plan.
* Provide a full audit trail of encryption key administration events
* Allow only company administrators to administer keys.
* Protect data at rest using application layer encryption
A Security Engineer is evaluating options for encryption key management Why should the Security Engineer choose AWS CloudHSM over AWS KMS for encryption key management in this situation?
A. CloudHSM ensures that only company support staff can administer encryption keys, whereas AWS KMS allows AWS staff to administer keys
B. CloudHSM provides the ability to copy keys to a different Region, whereas AWS KMS does not
C. The key administration event logging generated by CloudHSM is significantly more extensive than AWS KMS.
D. The ciphertext produced by CloudHSM provides more robust protection against brute force decryption attacks than the ciphertext produced by AWS KMS
Answer: A