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NEW QUESTION: 1
The system offers you two process control heuristics, SAP_MRP_001 and SAP_MRP_002, when setting up the multilevel MRP run in APO-PP/DS. The APO master data includes finished products with planning procedure "4" (planning in the planning run) and components with planning procedure "3" (cover dependant requirements immediately).
What difference do you expect in the behavior of the control heuristics in this case?
A. Only SAP_MRP_001 proceeds according to low-level coding, whereas SAP_MRP_002 proceeds with procedure "3" only.
B. If SAP_MRP_001 can only create alerts at component level, SAP_MRP_002 can resolve these and propagate them to the finished product level.
C. Components with procedure "3" are preferred by SAP_MRP_001. SAP_MRP_002 does not have a preference.
D. SAP_MRP_001 schedules the orders for products with procedure "4" infinitely, whereas SAP_MRP_002 schedules such orders finitely.
Answer: B

NEW QUESTION: 2
Case Study 5 - Wide World Importers
Background
Wide World Importers is moving all their datacenters to Azure. The company has developed several applications and services to support supply chain operations and would like to leverage serverless computing where possible.
Current environment
Windows Server 2016 virtual machine
This virtual machine (VM) runs Biz Talk Server 2016. The VM runs the following workflows:
* Ocean Transport - This workflow gathers and validates container information including container contents and arrival notices at various shipping ports.
* Inland Transport - This workflow gathers and validates trucking information including fuel usage, number of stops, and routes.
The VM supports the following REST API calls:
* Container API - This API provides container information including weight, contents, and other attributes.
* Location API - This API provides location information regarding shipping ports of call and truck stops.
* Shipping REST API - This API provides shipping information for use and display on the shipping website.
Shipping Data
The application uses MongoDB JSON document storage database for all container and transport information.
Shipping Web Site
The site displays shipping container tracking information and container contents. The site is located at http://shipping.wideworldimporters.com Proposed solution The on-premises shipping application must be moved to Azure. The VM has been migrated to a new Standard_D16s_v3 Azure VM by using Azure Site Recovery and must remain running in Azure to complete the BizTalk component migrations. You create a Standard_D16s_v3 Azure VM to host BizTalk Server. The Azure architecture diagram for the proposed solution is shown below:

Shipping Logic App
The Shipping Logic app must meet the following requirements:
* Support the ocean transport and inland transport workflows by using a Logic App.
* Support industry-standard protocol X12 message format for various messages including vessel content details and arrival notices.
* Secure resources to the corporate VNet and use dedicated storage resources with a fixed costing model.
* Maintain on-premises connectivity to support legacy applications and final BizTalk migrations.
Shipping Function app
Implement secure function endpoints by using app-level security and include Azure Active Directory (Azure AD).
REST APIs
The REST API's that support the solution must meet the following requirements:
* Secure resources to the corporate VNet.
* Allow deployment to a testing location within Azure while not incurring additional costs.
* Automatically scale to double capacity during peak shipping times while not causing application downtime.
* Minimize costs when selecting an Azure payment model.
Shipping data
Data migration from on-premises to Azure must minimize costs and downtime.
Shipping website
Use Azure Content Delivery Network (CDN) and ensure maximum performance for dynamic content while minimizing latency and costs.
Issues
Windows Server 2016 VM
The VM shows high network latency, jitter, and high CPU utilization. The VM is critical and has not been backed up in the past. The VM must enable a quick restore from a 7-day snapshot to include in-place restore of disks in case of failure.
Shipping website and REST APIs
The following error message displays while you are testing the website:

Hotspot Question
You need to correct the VM issues.
Which tools should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Backup and Restore: Azure Backup
Scenario: The VM is critical and has not been backed up in the past. The VM must enable a quick restore from a 7-day snapshot to include in-place restore of disks in case of failure.
In-Place restore of disks in IaaS VMs is a feature of Azure Backup.
Performance: Accelerated Networking
Scenario: The VM shows high network latency, jitter, and high CPU utilization. Accelerated networking enables single root I/O virtualization (SR-IOV) to a VM, greatly improving its networking performance. This high-performance path bypasses the host from the datapath, reducing latency, jitter, and CPU utilization, for use with the most demanding network workloads on supported VM types.
References:
https://azure.microsoft.com/en-us/blog/an-easy-way-to-bring-back-your-azure-vm-with-in-place- restore/

NEW QUESTION: 3
IPSEC WEB configuration wizard which does not support the following scenarios?
A. Branch Gateway
B. Gateway to Gateway
C. Host and Host
D. Gateway Center
Answer: C

NEW QUESTION: 4
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.
We also need environments in which our data scientists can carefully study and quickly adapt our models.
Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
B. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
C. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
D. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
Answer: D