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NEW QUESTION: 1
Which option is a chargeable feature in the Storwize V7000?
A. Real-time Compression
B. Easy Tier
C. Thin Provisioning
D. FlashCopy
Answer: D
Explanation:
Topic 2, Volume B
NEW QUESTION: 2
Case Study 2 - MJTelco
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
A. The number of workers
B. The disk size per worker
C. The maximum number of workers
D. The zone
Answer: C
NEW QUESTION: 3
You are a professional level SQL Server 2005 database administrator in an international corporation named Wiikigo. You are experienced in implementing high-availability solutions, monitoring the database server, and design deployment. In the company, you major job is the deployment, maintenance and support of Microsoft SQL Server 2005. You work as a database administrator for your company. More than a month ago, the SQL 2005 Server computer is deployed in operation. There are some query performance problems last week. Therefore, you perform an investigation on the locking contention. From the sys.dm_os_wait_stats dynamic management view (DMV), you can see a high value in the max_wait_time_ms column. You want to check out whether this value is a factor which leads to the current performance problems. In addition, the impact on database users should e cut to the least. Which action should be performed to achieve the goal?
A. To achieve the goal, the SQL Server Service should be restarted.
B. To achieve the goal, the SQL Server computer should be restarted.
C. To achieve the goal, the UPDATE STATISTICS command should be excuted.
D. To achieve the goal, the statistics in the dynamic management view (DMV) should be reset.
Answer: D
NEW QUESTION: 4
システム管理者は、侵害されたサーバーから次の情報を確認しています。
上記の情報から、リモートバッファオーバーフロー攻撃を介してMOSTが悪用された可能性のあるプロセスは次のうちどれですか？
A. TFTP
B. Apache
C. MySQL
D. LSASS
Answer: B