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NEW QUESTION: 1
展示を参照してください。
vSmartポリシーCisco SD-WAN vManage構成APIを非アクティブ化するには、Pythonスクリプトを作成する必要があります。
ドキュメントには、URLがPOSTを使用したこのREST呼び出しの展示に示されているとおりであり、
「policyId」は必須のリクエストパラメータです。変数を仮定して、Pythonコードのどの行がこの呼び出しを行うか
「s」は有効なリクエストセッションオブジェクトであり、変数「policy-id」はpolicyIdですか。
A. s.port('https://vmanage:8443/dataservice/template/policy/vsmart/activate/%s' % policy_id)
B. s.port('https://vmanage:8443/dataservice/template/policy/vsmart/activate?policyId=%s' % policy_id)
C. s.port('https://vmanage:8443/dataservice/template/policy/vsmart/activate&policyId=%s' % policy_id)
D. s.port('https://vmanage:8443/dataservice/template/policy/vsmart/activate/', data = {'policyId':
policy_id})
Answer: A
NEW QUESTION: 2
Which Cisco technology secures the network through malware filtering, category-based control, and reputation-based control?
A. Cisco IPS
B. Cisco WSA
C. Cisco remote-access VPNs
D. Cisco ASA 5500 Series appliances
Answer: B
NEW QUESTION: 3
Match the Azure service to the correct description.
Instructions: To answer, drag the appropriate Azure service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Select and Place:
Answer:
Explanation:
Explanation:
References:
https://azure.microsoft.com/en-us/services/sql-database/
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/hadoop/apache-hadoop-introduction
NEW QUESTION: 4
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注:正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to
"fit" your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features