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NEW QUESTION: 1
You have a complex form that you want to break down into smaller sections with a horizontal line and a label in "bold". Which action achieves the stated objective?
A. Include < br > < hr > < strong > < /strong > tags In Region Source section of the region
B. Place a Stop and start table item between form elements and set the label for each of these Stop and start table item using a label template
C. Change the region template to "Form Region"
D. Include multiple < br > tags between form elements
Answer: B

NEW QUESTION: 2
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. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
B. Use the NOW () function in BigQuery to record the event's time.
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
Explanation:
Topic 3, 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.

NEW QUESTION: 3
프로비저닝 된 처리량은 DynamoDB 테이블에서 데이터를 읽을 때 선택한 일관성 모델의 영향을 어떻게 받습니까?
A. 매우 일관된 읽기는 읽기 활동에 따라 가변 처리량을 사용합니다.
B. 강력하게 일관된 읽기는 결국 일관된 읽기보다 처리량이 적습니다.
C. 강력하게 일관된 읽기는 결국 일관된 읽기와 동일한 양의 처리량을 사용합니다.
D. 강력하게 일관된 읽기는 결국 일관된 읽기보다 더 많은 처리량을 사용합니다.
Answer: D

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