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NEW QUESTION: 1
What is the lowest level in a work breakdown structure?
A. None
B. w°rl< package
C. Project task
D. sow
Answer: B
Explanation:
A work package is the lowest level of a work breakdown structure.

NEW QUESTION: 2
What are the benefit of using Netflow? (Choose three.)
A. Network Planning
B. Network, Application & User Monitoring
C. Accounting/Billing
D. Security Analysis
Answer: B,C,D
Explanation:
Explanation/Reference:
NetFlow Definitions and Benefits
Reference:
http://www.cisco.com/en/US/products/sw/netmgtsw/ps1964/
products_implementation_design_guide09186a00800d6a11.html#wp1030045
Explanation:
NetFlow traditionally enables several key customer applications including:
Network Monitoring-NetFlow data enables extensive near real time network monitoring capabilities.
Flow-based analysis techniques may be utilized to visualize traffic patterns associated with individual routers and switches as well as on a network-wide basis (providing aggregate traffic or application based views) to provide proactive problem detection, efficient troubleshooting, and rapid problem resolution.
Application Monitoring and Profiling-NetFlow data enables network managers to gain a detailed, time- based, view of application usage over the network. This information is used to plan, understand new services, and allocate network and application resources (e.g. Web server sizing and VoIP deployment) to responsively meet customer demands.
User Monitoring and Profiling-NetFlow data enables network engineers to gain detailed understanding of customer/user utilization of network and application resources. This information may then be utilized to efficiently plan and allocate access, backbone and application resources as well as to detect and resolve potential security and policy violations.
Network Planning-NetFlow can be used to capture data over a long period of time producing the opportunity to track and anticipate network growth and plan upgrades to increase the number of routing devices, ports, or higher- bandwidth interfaces. NetFlow services data optimizes network planning including peering, backbone upgrade planning, and routing policy planning. NetFlow helps to minimize the total cost of network operations while maximizing network performance, capacity, and reliability. NetFlow detects unwanted WAN traffic, validates bandwidth and Quality of Service (QOS) and allows the analysis of new network applications. NetFlow will give you valuable information to reduce the cost of operating your network.
Security Analysis-NetFlow identifies and classifies DDOS attacks, viruses and worms in real-time.
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Accounting/Billing-NetFlow data provides fine-grained metering (e.g. flow data includes details such as IP addresses, packet and byte counts, timestamps, type-of-service and application ports, etc.) for highly flexible and detailed resource utilization accounting. Service providers may utilize the information for billing based on time-of-day, bandwidth usage, application usage, quality of service, etc. Enterprise customers may utilize the information for departmental charge-back or cost allocation for resource utilization.
NetFlow Data Warehousing and Data Mining-NetFlow data (or derived information) can be warehoused for later retrieval and analysis in support of proactive marketing and customer service programs (e.g. figure out which applications and services are being utilized by internal and external users and target them for improved service, advertising, etc.). In addition, NetFlow data gives Market Researchers access to the "who", "what", "where", and "how long" information relevant to enterprises and service providers.

NEW QUESTION: 3
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、記載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もあれば、正しい解決策がない場合もあります。
質問に答えた後、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。
ストリーミングデータを生成するAzure IoT Edgeデバイスがあります。
デバイスでは、Azure Machine Learningモデルを使用してデータの異常を検出する必要があります。異常が検出されたら、デバイスは異常に関する情報をAzure IoT Hubストリームに追加する必要があります。
解決策：Machine LearningモデルをAzure Webサービスとして公開します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection