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CS202: Discrete Structures Certification Exam Answers

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CS202: Discrete Structures Exam Quiz Answers

  • X = {1, 3, 9, 7}; Y = {3, 1, 7}
  • X = {1, 3, 9, 7}; Y = {3, 1, 5, 9}
  • X = {1, 3, 9, 7}; Y = {3, 1, 7, 9}
  • X = {1, 3, 9, 7}; Y = {1, 3, 9, 7, 11}
CS202 - Discrete Structures
  • Y is equal to X
  • Y is a subset of X
  • Y is a superset of X
  • Y is equal to the null set
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  • W – X – Y = Z
  • W ∪ X ∪ Y = Z
  • W X Y = Z
  • W ⊕ X ⊕ Y = Z
  • A
  • B
  • A B
  • The empty set
  • Elements in set X depend on specific elements in set Y
  • Elements in set X must be independent of elements in set Y
  • Elements in set X must entirely overlap with elements in set Y
  • Elements in set X must be completely different from elements in set Y
  • 6
  • 7
  • 8
  • 9
  • It can only meaningfully be called “completely true”
  • It can meaningfully be either “completely true” or “completely false”
  • It is “partially true”, where “partially” refers to some degree of truth
  • It is “partially false”, where “partially” refers to some degree of falsehood
  • True
  • False
  • Cannot be determined
  • This is not a logical proposition
  • E4 = E1 ^ E2 ^ E3
  • E4 = E1 v E2 v E3
  • E4 ⇔ E1 ⇔ E2 ⇔ E3
  • E4 = ᆨE1 ^ ᆨE2 ^ ᆨE3
  • L = (P ^ F) ^ (S ^ (D ^ I))
  • L = (P ^ F) ^ (S (D ^ I))
  • L = (P ∨ F) ∨ (S ^ (D ∨ I))
  • L = (P ∨ F) ∨ (S ∨ (D ∨ I))
  • Mary has been enabled by Susan
  • Mary has been enabled by Michael
  • Mary has an implied role in Susan’s success
  • Mary has nothing to do with Susan’s success
  • Showing P (0)
  • Showing P(n)
  • Proving P(n-1) ⟹ P(n), n = 1, 2, 3, ….
  • Proving P(n) ⟹ P (n + 1), n ∈ ℕ, the natural numbers
  • 3/13
  • 4/51
  • 4/51
  • 11/52
  • 0
  • 1/ (210)
  • 1/10
  • ½
  • 1/6
  • 1/3
  • 1/2
  • 6/2
  • f(x) = 2x
  • f(y) = (2y) – 1
  • f(n) = f(n-1) + 1
  • P (A ∪ B) = P(A) + P(B), where A and B are disjoint and P is the probability function
  • 35,156
  • -58,593
  • -292,968
  • 15 is not a member of the described sequence
  • st = f(st-1)
  • st = 0.002 * st-1
  • st = 0.99998 * st-1
  • st = st-1 – 0.99998
  • Edges and links
  • Vertices and edges
  • Vertices and nodes
  • Directional notations
  • Any graph that contains a circuit
  • A graph with a path whose vertex list contains every vertex of the graph
  • A graph that contains a circuit, touching each edge of the circuit exactly once
  • A graph with a path whose vertex list contains each vertex of the graph exactly once
1
2
3
CS202 - Discrete Structures
TimeSensor Value
00
12
220
325
426
527
630
731
831
932
1033
  • Assessment State
  • Initial State
  • Operational State
  • Shutdown State
Vertex ListEdge List
V1{v1, v2}
V2{v2, v1}
V3{v2, v3}
V4{v3, v4}
{v4, v3}
{v4, v1}
A
B
C
D
1
2
3
4
  • There are only two cycles
  • There are only two subtrees
  • Each vertex has one or more subtrees
  • Each vertex has no more than two subtrees
CS202 - Discrete Structures
  • A system terminal is active. It remains active until logout. Upon successful login, the system is accessible until logout.
  • A system terminal sits idle. It remains idle until login is attempted. Two failed login attempts are allowed. The third causes the terminal to become disabled. Reset by a system administrator is then required for the terminal to be re-enabled. Upon successful login, the system is accessible until logout.
  • A system terminal sits idle. It remains idle until login is attempted. Two failed login attempts are allowed. The third causes the terminal to become disabled. Automatic reset then occurs after five minutes so that the terminal is re-enabled. Upon successful login, the system is accessible until logout.
  • A system administrator enables an access terminal. The terminal then sits idle until login is attempted. Two failed login attempts are allowed. The third causes the terminal to become disabled. Reset by a system administrator is then required for the terminal to be re-enabled. Upon successful login, the system is accessible until logout.
  • There can be multiple transitions specified for a given state data input or external action impacting the state; and for every data input or external action impacting a state, a transition specification must exist
  • There can be only one transition specified for a given state data input or external action impacting the state; and for every data input or external action impacting a state, a transition specification must exist
  • There can be multiple transitions specified for a given state data input or external action impacting the state; and for every data input or external action impacting a state, a transition specification need not exist since the system is assumed to remain in its present state until a transition event occurs
  • There can be only one transition specified for a given state data input or external action impacting the state; and for every data input or external action impacting a state, a transition specification need not exist since the system is assumed to remain in its present state until a transition event occurs
  • Finite
  • Infinite
  • A countable number of elements
  • Not countable, but also not infinite
  • Resolution
  • Modus Tollens
  • Modus Ponens
  • Conjunctive specialization
  • 10101010
  • 1010010100
  • 1001010010
  • 100!90!
pqpq
000
010
100
111
  • If any fact is true then the conclusion is true
  • The conclusion is true only when all facts are true
  • A conclusion can be chosen at random regardless of the facts
  • The conclusion is true when all facts are true or when no facts are true
  • True
  • False
  • The result is logically incongruent
  • Sheffer Stroke of (A ∪ B) and (C ∪ D)
  • Set it equal to the probability of the just-prior causal event
  • Divide the probabilities of the string of causal events into each other
  • Add the probabilities of the individual outcomes that lead up to the event
  • Multiply the probabilities of the individual outcomes that lead up to the event
  • P(A∩B) = P(A) P(B)
  • P(A∩B) = P(A) + P(B)
  • P(A∩B) = P(A) / P(B)
  • P(A∩B) = P(A) – P(B)
  • An undirected graph can be traversed in exactly one direction
  • A directed graph can be traversed in any direction from any node
  • An undirected graph can be traversed in any direction from any node
  • A directed graph has only one circuit with a traversal path is specified by directional notation
  • S12
  • S22
  • S1 + S2
  • S2 * S1
CS202 - Discrete Structures
  • Union of W, X, Y
  • Intersection of W, X, Y
  • W, X, Y are subtracted from each other
  • W, X, Y are subtracted from the universal set
  • They cannot be stated, and what will happen cannot be predicted
  • They can be stated, and the probability of all possible outcomes lies along a uniform distribution
  • They can be stated, but the actual outcome on any given trial cannot be predicted with any certainty
  • They can be stated, and the actual outcome on any given trial can be predicted within a specified probability
  • 25%
  • 30%
  • 50%
  • 60%
CS202 - Discrete Structures
  • A fully-connected graph is necessary for traversal
  • The design consumes appears sophisticated and is likely to be accepted
  • Physical traversal requires multiple paths should a given path fail or be bottlenecked
  • Application owners can charge for the additional computer time needed to determine the shortest path
A
B
C
D
CS202 - Discrete Structures
  • X = Ø
  • Y ↔️ X
  • Y ⟶ X
  • X = ⇁Y
  • Resolution
  • Specialization
  • Generalization
  • Modus Ponens
  • AA−b
  • (A−b)!
  • P (A, A−b)
  • (A/A−b)
  • E4 = E1 ^ E2 ^ E3
  • E4 = E1 v E2 v E3
  • E4 ⊕ E1 ⊕ E2 ⊕ E3
  • E4 = ᆨE1 v ᆨE2 v ᆨE3
  • ¬A ∨ ¬B ^ C ^ D
  • ¬A ^ ¬B C D
  • A ^ B ∨ ¬C ∨ ¬D
  • ¬A ^ ¬B ∨ ¬C ∨ ¬D
  • C is a subset of D
  • D is a subset of C
  • For some x in D, P(x)
  • There exists an x in D
  • a → z
  • a ⇔ z
  • a z
  • a ⇏ z
  • Analogy
  • Deductive
  • Inductive
  • Reductive
  • 20%
  • 40%
  • 50%
  • 60%
  • No; you must also know the value of Fk for k = 0
  • No; you must also know the value of Fk for k = 1 and k = 2
  • Yes; the recursive sequence resolves itself as it proceeds
  • Yes; you know everything you need to perform the calculation
  • n + K, n ≥ 0
  • n + 1, n ≥ 0
  • n – K, n ≥ 0
  • n – k, n ≥ K
  • P = E*G(D)
  • Pt = Et*G(Dt)
  • Pt+1 = Et-1*G(Dt)
  • Pt+1 = Et-1*G(Dt-1)
  • A tree has no circular paths
  • A tree cannot be fully traversed
  • A tree enables direct access to all nodes
  • A tree has only one subgraph that contains a cycle
  • 1 + 1 = 0
  • 1 + 1 = 1
  • 1 + 1 = 2
  • 1 + 1 = 10
  • Conjunctive specialization
  • Disjunctive specialization
  • Elimination
  • Resolution
  • They have different base cases
  • They have different inductive steps
  • They require different levels of proof
  • They have different inductive assumptions
  • Uses far less memory than its explicit version
  • Can be calculated once the values of (a, b, c…) are known
  • Defines none of its input parameter values (a, b, …) in terms related to those same parameter values
  • Defines some or all of its input parameter values (a, b, …) in terms related to those same parameter values
  • A tree within a connected graph that connects all nodes of the graph
  • A subgraph of a connected graph that is a tree connecting all nodes of the graph
  • A subgraph of a connected graph that connects all nodes of the graph with a minimum number of edges
  • A subgraph of a connected graph that is a tree connecting all nodes of the graph with a minimum number of edges
  • Resolution
  • Elimination
  • Disjunctive specialization
  • Conjunctive specialization
  • 4
  • 8
  • ≥ 4
  • ≥ 8
  • Contains a circuit that touches every edge of the graph exactly once
  • Contains one or more circuits that touching each edge of the circuit exactly once
  • Contains a circuit that contains most edges of the graph, touching each only once
  • Has no circuits, and the graph can be traversed by touching every vertex exactly once
  • Vertices, edges, edges with or without direction
  • Nodes, links, edges without direction, no cycles
  • Nodes, links, edges with or without direction, no cycles
  • Nodes, links, edges with direction, only one cyclic subtree
  • A graph is a tree
  • A tree is a graph
  • A graph is directed
  • A tree can have a loop

Select one:

Origin StateTarget StateSensor Data Value
V1V210
V2V430
V2V330
V3V440
V4V320
V4V130
Origin StateTarget StateSensor Data Value
V1V210
V2V150
V2V630
V6V340
V3V230
V2V430
V4V530
V5V650
Origin StateTarget StateSensor Data Value
V1V210
V2V150
V2V630
V6V340
V3V230
V2V430
V4V530
V5V650
Origin StateTarget StateSensor Data Value
V1V210
V2V150
V2V630
V6V340
V3V230
V2V420
V4V530
V5V650
  • The valve is closed
  • The valve is not closed
  • The valve remains in its present state
  • The valve will alternate between open and closed
  • By deduction on n for both series
  • By induction on n for the first series and induction on k for the second series
  • By induction on n2 for the first series and induction on kn for the second series
  • By induction on n for the first series and induction on both k and n for the second series
  • P(A)/P(B)
  • P(A∪B)/P(B)
  • P(A∩B)/P(A)
  • P(A∩B)/P(B)
  • S1 and S2 are independent
  • S1 and S2 are not independent
  • S1 and S2 were generated using a weak seed
  • S1 and S2 were generated using a strong seed
  • The result when n=0
  • The result when k=0
  • The result when k<0
  • The result when n=0 and k=0

Introduction to Discrete Structures

Discrete structures are fundamental concepts in mathematics and computer science that deal with countable, distinct, and separate entities. They are central to the study of algorithms, data structures, and various other aspects of computing and discrete mathematics. Here are some key topics within discrete structures:

  1. Set Theory: The study of sets, which are collections of distinct objects. Key concepts include set operations (union, intersection, difference), subsets, power sets, and Cartesian products.
  2. Logic: The study of formal reasoning. It involves propositional logic (dealing with statements that are either true or false) and predicate logic (which deals with predicates and quantifiers). Topics include logical operators, truth tables, logical equivalence, and inference rules.
  3. Functions and Relations: Functions are mappings from one set to another, while relations are generalizations of functions that associate elements of one set with elements of another (or the same) set. Concepts include domain, range, injectivity, surjectivity, bijectivity, and composition of functions.
  4. Combinatorics: The study of counting, arrangement, and combination of discrete structures. It includes topics like permutations, combinations, the pigeonhole principle, and binomial coefficients.
  5. Graph Theory: The study of graphs, which are mathematical structures used to model pairwise relations between objects. Topics include vertices, edges, paths, cycles, connectivity, and graph coloring.
  6. Trees: A type of graph with no cycles, commonly used in data structures. Key concepts include binary trees, tree traversal methods, and applications in hierarchical data representation.
  7. Algorithms: The study of step-by-step procedures for performing computations. Important aspects include sorting and searching algorithms, algorithm complexity (time and space), and algorithm design paradigms (such as divide and conquer, dynamic programming, and greedy algorithms).
  8. Number Theory: The study of properties of integers. Important topics include divisibility, primes, congruences, and modular arithmetic.
  9. Boolean Algebra: The study of algebraic structures that capture the essence of logical operations. It involves binary variables and operations such as AND, OR, NOT, and their properties.
  10. Formal Languages and Automata: The study of abstract machines and languages. Topics include finite automata, regular expressions, context-free grammars, and Turing machines.

These topics are foundational for understanding and solving problems in computer science and discrete mathematics. Each area offers a rich set of concepts and techniques that are widely used in theoretical and practical applications.

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