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NEW QUESTION: 1
온라인 사진 앨범 앱에는 고품질 디스플레이가 있는 여러 화면 (예 : 데스크톱, 휴대 전화 및 태블릿)을 지원하는 주요 디자인 기능이 있습니다. 여러 버전의 이미지는 다른 해상도와 레이아웃으로 저장해야 합니다.
이미지 처리 Java 프로그램은 이미지 크기 및 형식에 따라 업로드 당 평균 5 초가 소요됩니다. 각 이미지 업로드는 사용자, 앨범, 사진 레이블, 업로드 타임 스탬프와 같은 이미지 메타 데이터를 캡처합니다.
앱에서 다음 요구 사항을 지원해야 합니다.
초당 수백 개의 사용자 이미지 업로드
* 최대 이미지 업로드 크기 : 10MB
* 최대 이미지 메타 데이터 크기는 1KB
* 지원되는 모든 화면에서 이미지가 최적화된 해상도로 표시됩니다.
* 이미지 업로드 후 1 분 이상
이러한 요구 사항을 충족시키기 위해 어떤 전략을 사용해야 합니까?
A. 이미지 및 메타 데이터를 Amazon Kinesis에 씁니다. Spark Streaming과 함께 Amazon Elastic MapReduce (EMR)를 사용하여 이미지 처리를 실행하고 이미지 출력을 Amazon S3에 저장하고 메타 데이터를 앱 저장소 DB에 저장합니다.
B. 이미지 및 메타 데이터를 Amazon Kinesis에 씁니다. Kinesis 클라이언트 라이브러리 (KCL) 응용 프로그램을 사용하여 이미지 처리를 실행하고 이미지 출력을 Amazon S3에 저장하고 메타 데이터를 앱 저장소 DB에 저장합니다.
C. BLOB 데이터 형식으로 이미지 및 메타 데이터 RDS를 작성합니다. AWS Data Pipeline을 사용하여 이미지 처리를 실행하고 이미지 출력을 Amazon S3에 저장하고 메타 데이터를 앱 저장소 DB에 저장합니다.
D. 메타 데이터가있는 이미지를 Amazon S3에 업로드하고 람다 (Lambda) 함수를 사용하여 이미지 처리를 실행하고 이미지 출력을 Amazon S3에 저장하고 메타 데이터를 앱 저장소 DB에 저장합니다.
Answer: D

NEW QUESTION: 2
Which H.245 information is exchanged within H.225 messages in H.323 Fast Connect?
A. Master-Slave Determination
B. Call Progress
C. Open Logical Channel
D. Call Setup
E. Terminal Capability Set
Answer: C

NEW QUESTION: 3
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
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 is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
A. Use the NOW () function in BigQuery to record the event's time.
B. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
C. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
Answer: C