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NEW QUESTION: 1
The headend has several modulators assigning frequencies for distribution to the CATV network. What distribution device should you use immediately at the headend to distribute the modulator's output signals to CATV system?
A. Combiner
B. Multiport tap
C. Directional coupler
D. Splitter
Answer: A

NEW QUESTION: 2
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 visualizations for operations teams with the following requirements:
* The report must include telemetry data from all 50,000 installations for the most resent 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.
Which approach meets the requirements?
A. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
B. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
D. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
Answer: C

NEW QUESTION: 3
A mediation flow had been deployed that contains several Event Emitter primitives to emit CEI events that will be used for audit tracking purposes. Currently, each of the CEI events emitted contains a copy of the entire message. During testing of the application, the systems administrator reported back that the server is performing poorly. The systems administrator has determined that the problem is caused by the size of the CEI events generated by the mediation flow. What could the developer do in this situation to improve performance?
A. Use a Message Filter primitive to filter out the message body before it reaches the Event Emitter primitives.
B. Promote the Enabled property of each Event Emitter primitive and set it to unchecked.
C. Change the transaction mode to 'New' to increase CEI performance.
D. Change the Root property of the Event Emitter primitives.
Answer: D

NEW QUESTION: 4
View the exhibit and examine the output.
Which statement can be an inference from the output?
Exhibit:

A. The number of times that the backup or restore process made an OS call to poll for I/O completion in Nonblocking mode is the least for the FRA disk group
B. The least number of I/Os are performed on the last data file in the list
C. The number of times that the backup or restore process directed the OS to wait until an I/O was complete is the highest for the last data file in the list
D. The FRA disk group has an asynchronous I/O bottleneck
Answer: D
Explanation:
NO EXHIBIT - ANSWER impossible / A) is set as placeholder Oracle Press 1Z0-053 Exam Guide, Chapter 8: Monitoring and Tuning RMAN Monitoring Asynchronous I/O
To monitor asynchronous I/O operations, you use the dynamic performance view V$BACKUP_ASYNC_IO. The key columns to watch are the following:
-IO_COUNT: Number of I/Os performed on the file
-LONG_WAITS: Number of times the backup or restore process had to tell the OS to wait for the I/O to complete
-SHORT_WAIT_TIME_TOTAL: Total time, in hundredths of a second, taken for nonblockingpolling for I/O completion The largest ratio of LONG_WAITS to IO_COUNT is a likely bottleneck in the backup process. SHORT_WAIT_TIME_TOTAL and LONG_WAIT_TIME_TOTAL are also indicators of a bottleneck if they are nonzero.

For these two files, you may consider increasing the multiplexing to decrease or eliminate the wait times when backing them up.
Exam Tip:
If you are using synchronous I/O but you have set BACKUP_DISK_IO_SLAVES to TRUE, then the I/O performance is monitored in V$BACKUP_ASYNC_IO.

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