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NEW QUESTION: 1
オンライン小売業者は、顧客が配達のために宅配便を選択することを可能にします。利用可能な宅配便サービスのリストは、Pegaデータベースで提供されたデータ型から取得されます。フルフィルメント部門は、地元の倉庫管理者が宅配便サービスを追加して配達時間の増加を軽減し、配達基準を満たしていない宅配便サービスを削除できるようにしたいと考えています。
地元の倉庫管理者が生産中の宅配便記録を更新できるようにするために必要な2つのオプションはどれですか？ （2つ選択してください。）
A. ローカル倉庫管理者のアクセスグループを作成します。
B. ローカル倉庫マネージャーのルールチェックアウトを有効にします。
C. 宅配便データ型のレコードを委任します。
D. 宅配便サービスを追加および削除するための承認プロセスを定義します。
Answer: C,D

NEW QUESTION: 2
SCENARIO - Please use the following to answer the next question:
It should be the most secure location housing data in all of Europe, if not the world. The Global Finance Data Collective (GFDC) stores financial information and other types of client data from large banks, insurance companies, multinational corporations and governmental agencies. After a long climb on a mountain road that leads only to the facility, you arrive at the security booth. Your credentials are checked and checked again by the guard to visually verify that you are the person pictured on your passport and national identification card.
You are led down a long corridor with server rooms on each side, secured by combination locks built into the doors. You climb a flight of stairs and are led into an office that is lighted brilliantly by skylights where the GFDC Director of Security, Dr. Monique Batch, greets you. On the far wall you notice a bank of video screens showing different rooms in the facility. At the far end, several screens show different sections of the road up the mountain.
Dr. Batch explains once again your mission. As a data security auditor and consultant, it is a dream assignment: The GFDC does not want simply adequate controls, but the best and most effective security that current technologies allow.
!'We were hacked twice last year," Dr. Batch says, :'and although only a small number of records were stolen, the bad press impacted our business. Our clients count on us to provide security that is nothing short of impenetrable and to do so quietly. We hope to never make the news again." She notes that it is also essential that the facility is in compliance with all relevant security regulations and standards.
You have been asked to verify compliance as well as to evaluate all current security controls and security measures, including data encryption methods, authentication controls and the safest methods for transferring data into and out of the facility. As you prepare to begin your analysis, you find yourself considering an intriguing question: Can these people be sure that I am who I say I am?
You are shown to the office made available to you and are provided with system login information, including the name of the wireless network and a wireless key. Still pondering, you attempt to pull up the facility s wireless network, but no networks appear in the wireless list. When you search for the wireless network by name, however it is readily found What type of wireless network does GFDC seem to employ?
A. A reluctant network.
B. A wireless mesh network.
C. A hidden network.
D. A user verified network.
Answer: B

NEW QUESTION: 3
Azureストレージアカウントの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、csv_filesという名前のフォルダーが含まれ、その中に複数のコンマ区切り値（CSV）ファイルが格納されます。
./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、実験用の推定器を構成する必要があります。
推定器を構成するにはどのコードを使用する必要がありますか？

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