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NEW QUESTION: 1
You have an event in the Cisco Prime Data Center Network Manager. To which two valid statuses can you change the event? (Choose two.)
A. resolved
B. assigned
C. in progress
D. acknowledged
E. pending
F. closed
Answer: D,F

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.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. BigQuery and Cloud Bigtable
C. BigQuery and Cloud Storage
D. Cloud Bigtable and Cloud SQL
Answer: B

NEW QUESTION: 3
Refer to the exhibit. You must complete the configuration on R1 so that a maximum of three links can be used and fragmentation is supported. Which additional configuration accomplishes this task?
A. interface Multilink19
ip address 192.168.1.1 255.255.255.0
ppp multilink
ppp multilink group 19
ppp multilink links minimum 1
ppp multilink links maximum 3
ppp multilink interleave
B. interface Multilink19
ip address 192.168.1.1 255.255.255.0
ppp multilink
ppp multilink group 19
ppp multilink links maximum 3
ppp multilink fragment delay 20
ppp multilink interleave
C. interface Multilink19
ip address 192.168.1.1 255.255.255.252
ppp multilink
ppp multilink group 19
ppp multilink links maximum 3
ppp multilink interleave
D. interface Multilink19
ip address 192.168.1.1 255.255.255.0
ppp multilink
ppp multilink group 19
ppp multilink links maximum 3
ppp multilink fragment delay 20
Answer: A
Explanation:
Explanation/Reference:
Explanation:
The "ppp multilink interleave" command is needed to enable link fragmentation and Interleaving (LFI). The Cisco IOS Link Fragmentation and Interleaving (LFI) feature uses Multilink PPP (MLP). MLP provides a method of splitting, recombining, and sequencing datagrams across multiple logical data links. MLP allows packets to be fragmented and the fragments to be sent at the same time over multiple point-to-point links to the same remote address.
ppp multilink links maximum
To limit the maximum number of links that Multilink PPP (MLP) can dial for dynamic allocation, use the ppp multilink links maximum command in interface configuration mode.
Reference: http://www.cisco.com/c/en/us/td/docs/ios/12_2/qos/configuration/guide/fqos_c/qcflfi.html

NEW QUESTION: 4
Sie haben eine Data-Warehouse-Faktentabelle mit einem Clustered-Columnstore-Index.
Sie haben mehrere CSV-Dateien mit insgesamt 3 Millionen Datenzeilen.
Sie müssen die Daten in die Faktentabelle hochladen. Die Lösung muss die Delta-Gruppe vermeiden, wenn Sie die Daten importieren.
Welche Lösung erreicht das Ziel in kürzester Zeit?
A. Laden Sie die Quelldaten in eine Staging-Tabelle mit einem Clustered-Index für den Primärschlüssel. Kopieren Sie die Daten mit der Anweisung insert_select in die Faktentabelle.
B. Laden Sie die Quelldaten in eine Staging-Tabelle. Laden Sie die Daten mithilfe der Anweisung insert_select in die Faktentabelle und geben Sie die Tablock-Option in der Staging-Tabelle an.
C. Laden Sie die Quelldaten in die Faktentabelle, indem Sie bcp.exe ausführen und die Option _ Tablock angeben.
D. Laden Sie die Quelldaten mithilfe der Bulk-Insert-Anweisung in die Faktentabelle und geben Sie die Tablock-Option an.
Answer: A
Explanation:
Explanation
If you are loading data only to stage it before running more transformations, loading the table to heap table will be much faster than loading the data to a clustered columnstore table. In addition, loading data to a
[temporary table][Temporary] will also load much faster than loading a table to permanent storage.
A common pattern for data load is to load the data into a staging table, do some transformation and then load it into the target table using the following command INSERT INTO <columnstore index> SELECT <list of columns> FROM <Staging Table> This command loads the data into the columnstore index in similar ways to BCP or Bulk Insert but in a single batch. If the number of rows in the staging table < 102400, the rows are loaded into a delta rowgroup otherwise the rows are directly loaded into compressed rowgroup. One key limitation was that this INSERT operation was single threaded. To load data in parallel, you could create multiple staging table or issue INSERT/SELECT with non-overlapping ranges of rows from the staging table. This limitation goes away with SQL Server 2016 (13.x). The command below loads the data from staging table in parallel but you will need to specify TABLOCK.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-data-loading-guidance?vi