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NEW QUESTION: 1
Refer to the exhibit.

A customer has implemented dark fiber between two Cisco MDS SAN switches After the network is put into production, the servers in site A experience poor performance when accessing the storage array in site B While troubleshooting this issue, you discover the buffer credit applied on the interface that connects the MDS switches is incorrect Which buffer-to-buffer credit value is needed to optimize the performance of the storage array access?
A. 0
B. 1
C. 2
D. 3
Answer: D

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
履歴データに基づいて気象条件を予測するモデルを作成します。
データストアからデータを読み込み、処理されたデータを機械学習モデルのトレーニングスクリプトに渡すために、処理スクリプトを実行するパイプラインを作成する必要があります。
解決策：次のコードを実行します。

ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: B
Explanation:
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

NEW QUESTION: 3
Which data attributes are stored on the contract account on SAP S/4HANA? Note: There are 2 correct answers to this question.
A. Customer classification
B. Central data
C. Payment terms
D. Dunning procedure
Answer: C,D

NEW QUESTION: 4

A. Option F
B. Option C
C. Option B
D. Option D
E. Option A
F. Option E
Answer: A,D,F
Explanation:
Oracle Solaris 11 uses Automated Installer (AI) for unattended installations.
Unattended installations are possible by placing the contents of the AI Image media (or ISO image contents from a download) on an AI server.