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NEW QUESTION: 1
Examine the following partial outputs from two routing debug commands; then answer the question below.
# get router info kernel
tab=254 vf=0 scope=0type=1 proto=11 prio=0 0.0.0.0/0.0.0.0/0->0.0.0.0/0 pref=0.0.0.0
gwy=10.200.1.254 dev=2(port1)
tab=254 vf=0 scope=0type=1 proto=11 prio=10 0.0.0.0/0.0.0.0/0->0.0.0.0/0 pref=0.0.0.0
gwy=10.200.2.254 dev=3(port2)
tab=254 vf=0 scope=253type=1 proto=2 prio=0 0.0.0.0/0.0.0.0/.->10.0.1.0/24 pref=10.0.1.254
gwy=0.0.0.0 dev=4(port3)
# get router info routing-table all s*0.0.0.0/0 [10/0] via 10.200.1.254, portl [10/0] via 10.200.2.254, port2, [10/0]
dO.0.1.0/24 is directly connected, port3 dO.200.1.0/24 is directly connected, portl d0.200.2.0/24 is directly
connected, port2
Which outbound interface or interfaces will be used by this FortiGate to route web traffic from internal users to the
Internet?
A. Both portl and port2.
B. port3.
C. port2.
D. port!
Answer: C
NEW QUESTION: 2
You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Explanation:
In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
References:
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/
NEW QUESTION: 3
The following protocol encryption protocol is ( )
A. H.225
B. H.245
C. H.283
D. H.235
Answer: D
NEW QUESTION: 4
You have an enterprise data warehouse in Azure Synapse Analytics that contains a table named FactOnlineSales. The table contains data from the start of 2009 to the end of 2012.
You need to improve the performance of queries against FactOnlineSales by using table partitions. The solution must meet the following requirements:
- Create four partitions based on the order date.
- Ensure that each partition contains all the orders placed during a given calendar year.
How should you complete the T-SQL command? To answer, select the appropriate options in the answer area.
MOTE: I ach correct selection is worth one point.
Answer:
Explanation:
Explanation
CREATE TABLE [dbo].[FactInternetSales]
(
[ProductKey] int NOT NULL
, [OrderDateKey] int NOT NULL
, [CustomerKey] int NOT NULL
, [PromotionKey] int NOT NULL
, [SalesOrderNumber] nvarchar(20) NOT NULL
, [OrderQuantity] smallint NOT NULL
, [UnitPrice] money NOT NULL
, [SalesAmount] money NOT NULL
)
WITH
( CLUSTERED COLUMNSTORE INDEX
, DISTRIBUTION = HASH([ProductKey])
, PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES
(20000101,20010101,20020101
,20030101,20040101,20050101
)
)
)
;