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NEW QUESTION: 1
There are three inputs in the plan business analysis approach. Which one of the following is not one of the three inputs?
A. Organizational process assets
B. Business need
C. Expert judgment
D. Risk and rewards analysis
Answer: D
Explanation:
Explanation/Reference:
Risk and rewards analysis is not, of itself, an input to the business analysis approach process.
D, B, and A are incorrect. Following are the inputs in the plan business analysis approach:
Business need
Expert judgment
Organizational process assets

NEW QUESTION: 2
After running the calculate accrual process, you notice that 100 employees' balances have not been updated despite the process running to a successful status. Your customer has requested that the process should fail if any employee is in error.
What absence batch parameter should you decrease to achieve this requirement?
A. Minimum Errors
B. Maximum Errors
C. Errors
D. Thread Count
E. Total Threads
F. Thread Size
G. Chunk Size
Answer: B,E,G

NEW QUESTION: 3
What is the SDH equivalent of the SONET STS-12 framing format?
A. SDH STM-64
B. SDH STM-12
C. SDH STM-4
D. SDH STM-16
E. SDH STM-1
Answer: C
Explanation:
Explanation/Reference:

NEW QUESTION: 4
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E
B. オプションD
C. オプションA
D. オプションB
E. オプションC
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