Active
Directory was only in charge of centralized domain management. It
became an umbrella title for a broad range of directory-based
identity-related services
When a user logs into a
computer that is part of a Windows domain, Active Directory checks the
submitted password and determines whether the user is a system administrator or normal user.
A server running Active
Directory Domain Service (AD DS) role is called a domain controller. It authenticates and authorizes all users and computers in a Windows domain type network—assigning and enforcing security
policies for all computers and installing or updating software.
2)
Mention what are the new features in Active Directory (AD) of Windows server
2012?
dcpromo (Domain
Controller Promoter) with improved wizard: It allows you to view all the steps
and review the detailed results during the installation process
Enhanced
Administrative Center: Compared to the earlier version of active
directory, the administrative center is well designed in Windows 2012. The
exchange management console is well designed
Recycle
bin goes GUI: In windows server 12, there are now many ways to enable the
active directory recycle bin through the GUI in the Active Directory
Administrative Center, which was not possible with the earlier version
Fine
grained password policies (FGPP): In windows server 12 implementing FGPP
is much easier compared to an earlier It allows you to create
different password policies in the same domain
Windows
Power Shell History Viewer: You can view the Windows PowerShell commands
that relates to the actions you execute in the Active Directory Administrative
Center UI
3)
Mention which is the default protocol used in directory services?
The
default protocol used in directory services is LDAP (
Lightweight Directory Access Protocol).
4)
Explain the term FOREST in AD?
Forest
is used to define an assembly of AD domains that share a single schema for the
AD. All DC’s in the forest share this schema and is replicated in a
hierarchical fashion among them.
5)
Explain what is SYSVOL?
The SysVOL folder
keeps the server’s copy of the domain’s public files. The contents such
as users, group policy, etc. of the sysvol folders are replicated to
all domain controllers in the domain.
6)
Mention what is the difference between domain admin groups and enterprise
admins group in AD?
7)
Mention what system state data contains?
System
state data contains
Contains
startup files
Registry
Com
+ Registration Database
Memory
page file
System
files
AD
information
SYSVOL
Folder
Cluster
service information
8)
Mention what is Kerberos?
Kerberos
is an authentication protocol for network. It is built to offer strong
authentication for server/client applications by using secret-key cryptography.
9)
Explain where does the AD database is held? What other folders are related to
AD?
AD
database is saved in %systemroot%/ntds. In the same folder, you can also see
other files; these are the main files controlling the AD structures they are
dit
log
res
1.log
log
chk
10)
Mention what is PDC emulator and how would one know whether PDC emulator is
working or not?
PDC
Emulators: There is one PDC emulator per domain, and when there is a
failed authentication attempt, it is forwarded to PDC emulator. It acts
as a “tie-breaker” and it controls the time sync across the domain.
These
are the parameters through which we can know whether PDC emulator is working or
not.
Time
is not syncing
User’s
accounts are not locked out
Windows
NT BDCs are not getting updates
If
pre-windows 2000 computers are unable to change their passwords
11)
Mention what are lingering objects?
Lingering
objects can exists if a domain controller does not replicate for an
interval of time that is longer than the tombstone lifetime (TSL).
12)
Mention what is TOMBSTONE lifetime?
Tombstone
lifetime in an Active Directory determines how long a deleted object is
retained in Active Directory. The deleted objects in Active
Directory is stored in a special object referred as TOMBSTONE.
Usually, windows will use a 60- day tombstone lifetime if time is not set in
the forest configuration.
13)
Explain what is Active Directory Schema?
Schema
is an active directory component describes all the attributes and objects that
the directory service uses to store data.
14)
Explain what is a child DC?
CDC
or child DC is a sub domain controller under root domain controller which share
name space
15)
Explain what is RID Master?
RID
master stands for Relative Identifier for assigning unique IDs to the object
created in AD.
16)
Mention what are the components of AD?
Components
of AD includes
Logical
Structure: Trees, Forest, Domains and OU
Physical
Structures: Domain controller and Sites
17)
Explain what is Infrastructure Master?
Infrastructure
Master is accountable for updating information about the user and group and
global catalogue.
• Artificial Intelligence (AI) is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. There are three basic AI concepts: machine learning, deep learning, and neural networks.
• This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way.
• A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears
• Artificial intelligence can be viewed from a variety of perspectives.
Components of Artificial Intelligent
Applications
Image Recognition
Speech Recognition
chatbots
Natural Language generate
Sentiment Analysis
Software/Hardware for Training and Running models
GPUS
Parallels processing tools
Cloud data storage and compute platforms
Programming languages for building modules.
Python
Tensorflow
Java
C
From the perspective of intelligence artificial intelligence is making machines "intelligent" -- acting as we would expect people to act. o The inability to distinguish computer responses from human responses is called the Turing test. o Intelligence requires knowledge o Expert problem solving - restricting domain to allow including significant relevant knowledge
From a business perspective AI is a set of very powerful tools, and methodologies for using those tools to solve business problems.
From a programming perspective, AI includes the study of symbolic programming, problem solving, and search. o Typically AI programs focus on symbols rather than numeric processing. o Problem solving - achieve goals. o Search - seldom access a solution directly. Search may include a variety of techniques. o AI programming languages include: – LISP, developed in the 1950s, is the early programming language strongly associated with AI. LISP is a functional programming language with procedural extensions. LISP (LISt Processor) was specifically designed for processing heterogeneous lists -- typically a list of symbols. Features of LISP are run- time type checking, higher order functions (functions that have other functions as parameters), automatic memory management (garbage collection) and an interactive environment. – The second language strongly associated with AI is PROLOG. PROLOG was developed in the 1970s. PROLOG is based on first order logic. PROLOG is declarative in nature and has facilities for explicitly limiting the search space. – Object-oriented languages are a class of languages more recently used for AI programming. Important features of object-oriented languages include: concepts of objects and messages, objects bundle data and methods for manipulating the data, sender specifies what is to be done receiver decides how to do it, inheritance (object hierarchy where objects inherit the attributes of the more general class of objects). Examples of object-oriented languages are Smalltalk, Objective C, C++. Object oriented extensions to LISP (CLOS - Common LISP Object System) and PROLOG (L&O - Logic & Objects) are also used.
• Artificial Intelligence is a new electronic machine that stores large amount of information and process it at very high speed
• The computer is interrogated by a human via a teletype It passes if the human cannot tell if there is a computer or human at the other end
• The ability to solve problems
• It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence Importance of AI
• Game Playing You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.
• Speech Recognition In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
• Understanding Natural Language Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
• Computer Vision The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
• Expert Systems A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. The usefulness of current expert systems depends on their users having common sense. Heuristic Classification One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
Consumer Marketing o Have you ever used any kind of credit/ATM/store card while shopping? o if so, you have very likely been “input” to an AI algorithm o All of this information is recorded digitally o Companies like Nielsen gather this information weekly and search for patterns – general changes in consumer behavior – tracking responses to new products – identifying customer segments: targeted marketing, e.g., they find out that consumers with sports cars who buy textbooks respond well to offers of new credit cards. o Algorithms (“data mining”) search data for patterns based on mathematical theories of learning
Identification Technologies o ID cards e.g., ATM cards o can be a nuisance and security risk: cards can be lost, stolen, passwords forgotten, etc o Biometric Identification, walk up to a locked door – Camera – Fingerprint device – Microphone – Computer uses biometric signature for identification – Face, eyes, fingerprints, voice pattern – This works by comparing data from person at door with stored library – Learning algorithms can learn the matching process by analyzing a large library database off-line, can improve its performance.
Intrusion Detection o Computer security - we each have specific patterns of computer use times of day, lengths of sessions, command used, sequence of commands, etc – would like to learn the “signature” of each authorized user – can identify non-authorized users o How can the program automatically identify users? – record user’s commands and time intervals – characterize the patterns for each user – model the variability in these patterns – classify (online) any new user by similarity to stored patterns
Machine Translation o Language problems in international business – e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common language – If you are shipping your software manuals to 127 countries, the solution is ; hire translators to translate – would be much cheaper if a machine could do this! o How hard is automated translation – very difficult! – e.g., English to Russian – not only must the words be translated, but their meaning also! POWER OF AI !
