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NEW QUESTION: 1
会社には、10,000人のユーザーを含むcontoso.comという名前のAzure Active Directory（Azure AD）テナントがあります。
同社にはMicrosoft 365サブスクリプションがあります。
contoso.comのすべてのユーザーに対してAzure Multi-Factor Authentication（MFA）を有効にします。
次のクエリを実行します。
「SigninLogs」を検索| ResultDescription ==「ユーザーはMFAチャレンジに合格しませんでした。」クエリは空の結果を返します。
クエリが期待される結果を返すことを確認する必要があります。
あなたは何をするべきか？
A. セキュリティとコンプライアンスの管理センターから、監査を有効にします。
B. Azure Active Directory管理センターから、Azureストレージアカウントにログをアーカイブするための診断設定を構成します。
C. Azure Active Directory管理センターから、診断設定を構成して、Azure Log Analyticsワークプレースにログを送信します。
D. セキュリティとコンプライアンスの管理センターから、Office 365 Analyticsを有効にします。
Answer: C
Explanation:
You can now send audit logs to Azure Log Analytics. This gives you much easier reporting on audit events and the ability to perform queries such as the one in this question.
References:
https://docs.microsoft.com/en-us/azure/active-directory/reports-monitoring/howto-integrate-activity-logs-with-log-analytics

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. はい
B. いいえ
Answer: A
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
O: 26
Which two settings must be the same between the backup source and restore target with DRS in Cisco Unified Communications Manager? (Choose two.)
A. Server IP Address
B. NTP Servers
C. Server Hostname
D. Domain Name
E. Cluster Security Password
F. Certificate Information
Answer: A,C

NEW QUESTION: 4

A. 0
B. 1.0
C. 2.0
D. 4.0
Answer: B