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NEW QUESTION: 1
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of
their loads
Perform analytics on all their orders and shipment logs, which contain both structured and unstructured
data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments
Accurately track every shipment worldwide using proprietary technology
Improve business agility and speed of innovation through rapid provisioning of new resources
Analyze and optimize architecture for performance in the cloud
Migrate fully to the cloud if all other requirements are met
Technical Requirements
Handle both streaming and batch data
Migrate existing Hadoop workloads
Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
A. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
B. Store the common data encoded as Avro in Google Cloud Storage.
C. Store the common data in BigQuery as partitioned tables.
D. Store the common data in BigQuery and expose authorized views.
Answer: D
NEW QUESTION: 2
Which of the following security controls will you use for the deployment phase of the SDLC to build secure software Each correct answer represents a complete solution. Choose all that apply.
A. Security Certification and Accreditation (C&A)
B. Risk Adjustments
C. Vulnerability Assessment and Penetration Testing
D. Change and Configuration Control
Answer: A,B,C
NEW QUESTION: 3
Which two descriptions of the Call Home feature on a Cisco UCS server are true? (Choose two.)
A. provides all the information to administrators, who can open a case with one click
B. provides an email-based notification for critical system policies
C. uses Java to generate predefined reports
D. provides a central repository for storing fault data
E. delivers alerts to multiple recipients, including Cisco TAC, and generates a case by using Cisco TAC
Answer: B,E
Explanation:
Reference: http://www.cisco.com/c/en/us/td/docs/unified_computing/ucs/sw/gui/config/guide/131/b_UCSM_GUI_Configuration_Guide_1_3_1/UCSM_GUI_Configuration_Guide_1_3_1_chapter39.html
NEW QUESTION: 4
_________ of the amine system is the most effective way to prevent amine corrosion.
A. Proper operation
B. Proper startup
C. Proper design
D. Proper concentration
Answer: A