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NEW QUESTION: 1
Which statement about LISP encapsulation in an EIGRP OTP implementation is true?
A. LISP learns the next hop
B. OTP uses LISP encapsulation for dynamic multipoint tunneling
C. OTP maintains the LISP control plane
D. OTP uses LISP encapsulation to obtain routes from neighbors
Answer: C
Explanation:
Explanation
The EIGRP Over the Top solution can be used to ensure connectivity between disparate EIGRP sites. This feature uses EIGRP on the control plane and Locator ID Separation Protocol (LISP) encapsulation on the data plane to route traffic across the underlying WAN architecture. EIGRP is used to distribute routes between customer edge (CE) devices within the network, and the traffic forwarded across the WAN architecture is LISP encapsulated.
EIGRP OTP only uses LISP for the data plane, EIGRP is still used for the control plane. Therefore we cannot say OTP uses LISP encapsulation for dynamic multipoint tunneling as this requires encapsulating both data and control plane traffic -> Answer 'OTP uses LISP encapsulation for dynamic multipoint tunneling' is not correct.
In OTP, EIGRP serves as the replacement for LISP control plane protocols (therefore EIGRP will learn the next hop, not LISP -> Answer 'LISP learns the next hop' is not correct). Instead of doing dynamic EID-to-RLOC mappings in native LISP-mapping services, EIGRP routers running OTP over a service provider cloud create targeted sessions, use the IP addresses provided by the service provider as RLOCs, and exchange routes as EIDs. Let's take an example:

If R1 and R2 ran OTP to each other, R1 would learn about the network 10.0.2.0/24 from R2 through EIGRP, treat the prefix 10.0.2.0/24 as an EID prefix, and take the advertising next hop 198.51.100.62 as the RLOC for this EID prefix. Similarly, R2 would learn from R1 about the network 10.0.1.0/24 through EIGRP, treat the prefix 10.0.1.0/24 as an EID prefix, and take the advertising next hop 192.0.2.31 as the RLOC for this EID prefix. On both routers, this information would be used to populate the LISP mapping tables. Whenever a packet from 10.0.1.0/24 to 10.0.2.0/24 would arrive at R1, it would use its LISP mapping tables just like in ordinary LISP to discover that the packet has to be LISP encapsulated and tunneled toward 198.51.100.62, and vice versa. The LISP data plane is reused in OTP and does not change; however, the native LISP mapping and resolving mechanisms are replaced by EIGRP.
Reference: CCIE Routing and Switching V5.0 Official Cert Guide, Volume 1, Fifth Edition

NEW QUESTION: 2
You create a table that has the StudentCode, SubjectCode, and Marks columns to record mid-year marks for students. The table has marks obtained by 50 students for various subjects.
You need to ensure that the top half of the students arranged by their average marks must be given a rank of 1 and the remaining students must be given a rank of 2.
Which Transact-SQL query should you use?
A. SELECT StudentCode AS Code,Marks AS Value FROM (SELECT StudentCode, Marks AS Marks,RANK() OVER (PARTITION BY SubjectCode ORDER BY Marks ASC) AS RankFROM StudentMarks) tmpWHERE Rank
= 1
B. SELECT StudentCode as Code,DENSE_RANK() OVER (ORDER BY AVG (Marks) DESC) AS ValueFROM StudentMarksGROUP BY StudentCode
C. SELECT StudentCode AS Code,Marks AS Value FROM (SELECT StudentCode, Marks AS Marks,RANK () OVER (PARTITION BY StudentCode ORDER BY Marks ASC) AS RankFROM StudentMarks) tmpWHERE Rank = 1
D. SELECT StudentCode as Code,NTILE (2) OVER (ORDER BY AVG (Marks) DESC) AS ValueFROM StudentMarksGROUP BY StudentCode
E. SELECT StudentCode AS Code,Marks AS Value FROM (SELECT StudentCode, Marks AS Marks,RANXO OVER (PARTITION BY StudentCode ORDER BY Marks DESC) AS RankFROM StudentMarks) tmpWHERE Rank = 1
F. SELECT StudentCode as Code,RANK() OVER (ORDER BY AVG (Marks) DESC) AS ValueFROM StudentMarksGROUP BY StudentCode
G. SELECT StudentCode AS Code,Marks AS Value FROM (SELECT StudentCode, Marks AS Marks,RANK() OVER (PARTITION BY SubjectCode ORDER BY Marks DESC) AS RankFROM StudentMarks) tmpWHERE Rank = 1
H. SELECT Id, Name, Marks,DENSE_RANK() OVER (ORDER BY Marks DESC) AS RankFROM StudentMarks
Answer: D

NEW QUESTION: 3
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml