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NEW QUESTION: 1
See the Exhibit and examine the structure and data in the INVOICE table: Exhibit:

Which two SQL statements would executes successfully? (Choose two.)
A. SELECT (AVG(inv_date) FROM invoice;
B. SELECT MAX(inv_date),MIN(cust_id) FROM invoice;
C. SELECT MAX(AVG(SYSDATE - inv_date)) FROM invoice;
D. SELECT AVG(inv_date - SYSDATE),AVG(inv_amt) FROM invoice;
Answer: B,D

NEW QUESTION: 2
Lab question



A. conf t
int loopback 0
no shut
ipv4 address 10.7.1.1 255.255.255.255
ipv6 address 2001:db8:10:7:1::1/128
int Gi0/0/0/1
no shut
ipv4 address 192.168.178.70 255.255.255.0
ipv6 address 2001:DB8:192:168:178::70/64
commit
router bgp 65001
address-family ipv4 unicast
redistribute connected
neighbor 10.8.1.1
remote-as 65001
update-source loopback 0
commit
exit
address family ipv6 unicast
redistribute connected
neighbor 2001:db8:10:8:1::1
remote-as 65001
update-source loopback 0
commit
Answer: A
Explanation:
Explanation/Reference:

NEW QUESTION: 3
Which HTTP error code corresponds to the ProxySG default exception identifier dns_unresolved_hostname?
A. 0
B. 1
C. 2
D. 3
Answer: A

NEW QUESTION: 4
Which of the following are feature engineering techniques? (Select 2 answers)
A. Bucketization of a continuous feature
B. Crossed feature columns
C. Feature prioritization
D. Hidden feature layers
Answer: A,B
Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_mo del