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NEW QUESTION: 1
AzureStorageアカウントのBLOBコンテナーを参照するtraining_dataという名前のデータストアを作成します。 blobコンテナーには、複数のコンマ区切り値(CSV)ファイルが格納されているcsv_filesという名前のフォルダーが含まれています。
./scriptという名前のローカルフォルダーにtrain.pyという名前のスクリプトがあり、推定器を使用して実験として実行する予定です。スクリプトには、csv_filesフォルダーからデータを読み取るための次のコードが含まれています。
次のスクリプトがあります。
スクリプトがtraining_dataデータストアのcsv_filesフォルダーを参照するdata_refという名前のデータ参照からデータを読み取ることができるように、実験の推定値を構成する必要があります。
Estimatorを構成するためにどのコードを使用する必要がありますか?
A. オプションA
B. オプションD
C. オプションC
D. オプションB
E. オプションE
Answer: D
Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Incorrect Answers:
A: Pandas DataFrame not used.
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets
NEW QUESTION: 2
HOTSPOT
Topic 1, Web-based Solution
Background
You are developing a web-based solution that students and teachers can use to collaborate on written assignments. Teachers can also use the solution to detect potential plagiarism, and they can manage assignments and data by using locally accessible network shares.
Business Requirements
The solution consists of three parts: a website where students work on assignments and where teachers view and grade assignments, the plagiarism detection service, and a connector service to manage data by using a network share.
The system availability agreement states that operating hours are weekdays between midnight on Sunday and midnight on Friday.
Plagiarism Service
The plagiarism detection portion of the solution compares a new work against a repository of existing works. The initial dataset contains a large database of existing works. Teachers upload additional works. In addition, the service itself searches for other works and adds those works to the repository.
Technical Requirements
Website
The website for the solution must run on an Azure web role.
Plagiarism Service
The plagiarism detection service runs on an Azure worker role. The computation uses a random number generator. Certain values can result in an infinite loop, so if a particular work item takes longer than one hour to process, other instances of the service must be able to process the work item. The Azure worker role must fully utilize all available CPU cores. Computation results are cached in local storage resources to reduce computation time.
Repository of Existing Works
The plagiarism detection service works by comparing student submissions against a repository of existing works by using a custom matching algorithm. The master copies of the works are stored in Azure blob storage. A daily process synchronizes files between blob storage and a file share on a virtual machine (VM). As part of this synchronization, the ExistingWorkRepository object adds the files to Azure Cache to improve the display performance of the website. If a student's submission is overdue, the Late property is set to the number of days that the work is overdue. Work files can be downloaded by using the Work action of the TeacherController object
Network Connector
Clients can interact with files that are stored on the VM by using a network share. The network permissions are configured in a startup task in the plagiarism detection service.
Service Monitoring
The CPU of the system on which the plagiarism detection service runs usually limits the plagiarism detection service. However, certain combinations of input can cause memory issues, which results in decreased performance. The average time for a given computation is 45 seconds. Unexpected results during computations might cause a memory dump. Memory dump files are stored in the Windows temporary folder on the VM that hosts the worker role.
Security
Only valid users of the solution must be able to view content that users submit. Privacy regulations require that all content that users submit must be retained only in Azure Storage. All documents that students upload must be signed by using a certificate named DocCert that is installed in both the worker role and the web role.
Solution Development
You use Microsoft Visual Studio 2013 and the Azure emulator to develop and test both the compute component and the storage component. New versions of the solution must undergo testing by using production data.
Scaling
During non-operating hours, the plagiarism detection service should not use more than 40 CPU cores. During operating hours, the plagiarism detection service should automatically scale when 500 work items are waiting to be processed. To facilitate maintenance of the system, no plagiarism detection work should occur during non-operating hours. All ASP.NET MVC actions must support files that are up to 2 GB in size.
Biographical Information
Biographical information about students and teachers is stored in a Microsoft Azure SQL database. All services run in the US West region. The plagiarism detection service runs on Extra Large instances.
Solution Structure
Relevant portions of the solution files are shown in the following code segments. Line numbers in the code segments are included for reference only and include a two-character prefix that denotes the specific file to which the line belongs.
==================================================================
You need to configure scaling for the plagiarism detection service.
What should you do? To answer, select the appropriate values in the dialog box in the answer area.
Answer:
Explanation:
NEW QUESTION: 3
What is a potential advantage of vSCSI over NPIV?
A. Simpler VIO Server administration
B. Higher throughput
C. Higher number of LUNs supported
D. Minimized cost of vendor multi-path software
Answer: D