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NEW QUESTION: 1
Which two authentication stores are supported to design a wireless network using PEAP EAP- MSCHAPv2 as the authentication method? (Choose two.)
A. LDAP
B. Certificate Server
C. Microsoft Active Directory
D. RSA Secure-ID
E. ACS
Answer: C,E
Explanation:
ACS supports a variety of user databases. It supports the ACS internal database and several external user databases, including:
* Windows Active Directory
* LDAP
* RSA SecurID Servers
* RADIUS Identity Servers
This appendix describes the following:
RADIUS-based authentication that does not include EAP:
- PAP
- CHAP
- MSCHAPv1
- EAP-MSCHAPv2
https://www.cisco.com/c/en/us/td/docs/net_mgmt/cisco_secure_access_control_system/5-
8/user/guide/acsuserguide/eap_pap_phase.html
Active Directory
Supported Protocols: PAP, MsCHAP, PEAP, EAP-MsCHAPV2, and EAP-TTLS with tunneled PAP.
https://avayauc.wordpress.com/2019/03/22/ldap-authentication-xmc/

NEW QUESTION: 2
User1 creates table user1 .customers in a partitioned-by-growth (PBG) table space DB1.TS1 Which of the following operations CANNOT be performed by user1 with implicit owner privileges on the table?
A. Create a VIEW on the customers table
B. Run the LOAD utility to load data
C. Run the REORG utility to reorganize the data
D. Issue an SQL LOCK TABLE statement against the customers table
Answer: B

NEW QUESTION: 3
A Machine Learning Engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML Engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML Engineer do to minimize bias due to missing values?
A. For each feature, approximate the missing values using supervised learning based on other features.
B. Replace each missing value by the mean or median across non-missing values in same row.
C. Replace each missing value by the mean or median across non-missing values in the same column.
D. Delete observations that contain missing values because these represent less than 5% of the data.
Answer: A
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C.
Supervised learning applied to the imputation of missing values is an active field of research.