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Test: Functional Dependency - Computer Science Engineering (CSE) MCQ


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10 Questions MCQ Test Database Management System (DBMS) - Test: Functional Dependency

Test: Functional Dependency for Computer Science Engineering (CSE) 2024 is part of Database Management System (DBMS) preparation. The Test: Functional Dependency questions and answers have been prepared according to the Computer Science Engineering (CSE) exam syllabus.The Test: Functional Dependency MCQs are made for Computer Science Engineering (CSE) 2024 Exam. Find important definitions, questions, notes, meanings, examples, exercises, MCQs and online tests for Test: Functional Dependency below.
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Test: Functional Dependency - Question 1

We can use the following three rules to find logically implied functional dependencies. This collection of rules is called

Detailed Solution for Test: Functional Dependency - Question 1

By applying these rules repeatedly, we can find all of F+, given F.

Test: Functional Dependency - Question 2

Which of the following is not Armstrong’s Axiom?

Detailed Solution for Test: Functional Dependency - Question 2

It is possible to use Armstrong’s axioms to prove that Pseudotransitivity rule is sound.

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Test: Functional Dependency - Question 3

Inst_dept (ID, name, salary, dept name, building, budget) is decomposed into

instructor (ID, name, dept name, salary)
department (dept name, building, budget)

This comes under

Detailed Solution for Test: Functional Dependency - Question 3

Lossy-join decomposition is the decomposition used here .

Test: Functional Dependency - Question 4

Consider a relation R(A,B,C,D,E) with the following functional dependencies:

ABC -> DE and
D -> AB

The number of superkeys of R is:

Detailed Solution for Test: Functional Dependency - Question 4

A superkey is a combination of columns that uniquely identifies any row within a relational database management system (RDBMS) table.

Test: Functional Dependency - Question 5

Suppose relation R(A,B) currently has tuples {(1,2), (1,3), (3,4)} and relation S(B,C) currently has {(2,5), (4,6), (7,8)}. Then the number of tuples in the result of the SQL query:


<i>SELECT *
    FROM R NATURAL OUTER JOIN S; </i>IS:

Detailed Solution for Test: Functional Dependency - Question 5

The SQL NATURAL JOIN is a type of EQUI JOIN and is structured in such a way that, columns with same name of associate tables will appear once only.

Test: Functional Dependency - Question 6

Which of the following is the trivial functional dependency in A+, where A+ is closure of A?

Detailed Solution for Test: Functional Dependency - Question 6

The closure of F, denoted as F+, is the set of all regular FD, that can be derived from.

For trivial functional dependency,

Let A and be two sets consists of attributes of a relation

A → B

A ⊇ B 

Option 1: 

{X, Z} → {Z, W}

{X, Z}  ⊉ {Z, W}

Not a trivial functional dependency

Option 2: 

{X, V} → {V}

{X, V} ⊇ {V}

It is a trivial functional dependency

Option 3: 

{X, W, V} → {Y}

{X, W, V} ⊉  {Y}

Not a trivial functional dependency

Option 4: 

{Y, W } → {Y, X}

{Y, W } ⊉ {Y, X}

Not a trivial functional dependency

Test: Functional Dependency - Question 7

Suppose we wish to find the ID’s of the employees that are managed by people who are managed by the employee with ID 123. Here are two possible queries:

I.SELECT ee.empID
  FROM Emps ee, Emps ff
  WHERE ee.mgrID = ff.empID AND ff.mgrID = 123;
II.SELECT empID
  FROM Emps 
  WHERE mgrID IN
  (SELECT empID FROM Emps WHERE mgrID = 123);

Q. Which, if any, of the two queries above will correctly (in SQL2) get the desired set of employee ID’s?

Detailed Solution for Test: Functional Dependency - Question 7

The query can be satisfied by any of the two options.

Test: Functional Dependency - Question 8

The left side and the right side of a functional dependency are sometimes called __________and __________, respectively.

Detailed Solution for Test: Functional Dependency - Question 8

The left side and the right side of a functional dependency are sometimes called "determinant" and "dependent", respectively.

In a functional dependency, the determinant is the set of one or more attributes that determine the values of another set of attributes, called the dependent attributes. The determinant is typically on the left-hand side of the functional dependency arrow (→), while the dependent attributes are on the right-hand side.

For example, if we have a functional dependency A→B, it means that the values of attribute B are determined by the values of attribute A. In this case, A is the determinant and B is the dependent attribute.

The other options listed are not typically used to describe the left and right sides of a functional dependency. Here is a brief explanation of each:

  • Domain; Attributes - The domain refers to the set of all possible values that an attribute can take, while attributes are the specific properties of an entity or relation. These terms are not typically used to describe the left and right sides of a functional dependency.
  • Tuple; Relation - A tuple is a single row or record in a relation, while a relation is a set of tuples that share the same attributes. These terms are not typically used to describe the left and right sides of a functional dependency.
  • Range; Table - The range is the set of all possible output values of a function, while a table is a collection of related data stored in rows and columns. These terms are not typically used to describe the left and right sides of a functional dependency.

 
Therefore, the correct answer is "Determinant; Dependent".

Test: Functional Dependency - Question 9

Let R (ABCDEFGH) be a relation schema and F be the set of dependencies F = {A → B, ABCD → E, EF → G, EF → H and ACDF →EG}. The minimal cover of a set of functional dependencies is

Detailed Solution for Test: Functional Dependency - Question 9

Use the union rule to replace

EF → G and EF → H 

EF → GH

F = { A → B  ABCD → E  EF → GH  ACDF → EG }

B is extraneous in ABCD → E because B ∈ ABCD and {A → B, ABCD → E, EF → GH, ACDF → EG}

logically implies {A → B, ACD → E, EF → GH, ACDF → EG}.

This is because every  ACD → E.

This FD can be derived using Armstrong’s Axioms from A → B and ABCD → E via transitivity rule

So remove B from ABCD → E.

F = { A → B ACD → E  EF → GH ACDF → EG }

E is extraneous in ACDF → EG because E ∈ EG and {A → B, ACD → E, EF → GH, ACDF → G}

logically implies {A → B, ACD → E, EF → GH, ACDF → EG}

remove E from ACDF → EG

F = { A → B ACD → E EF → GH ACDF → G}

G is extraneous in ACDF → G. Note that ACDF → G is already implied by ACD → E and EF → GH in F

remove ACDF → G from F.

None of the remaining FD's in F have extraneous attributes so the minimal cover is

A → B, ACD → E, EF → G, EF → H.

Test: Functional Dependency - Question 10

In a database, a rule is defined as (P1 and P2) or P3? R1 (0.8) and R2 (0.3), where P1, P2, P3 are premises and R1. R2 are conclusions of rules with certainty factors (CF) 0.8 and 0.3 respectively. If any running program has produced P1, P2, P3 with CF as 0.5, 0.8. 0.2 respectively, find the CF of results on the basis of premises.

Detailed Solution for Test: Functional Dependency - Question 10

Certainty Factor (CF) is a numeric value that tells us how likely an event or a statement is supposed to be true.

This value is similar to probability, but with only probability value of any event agent cannot decide what to do. This certainty factor is decided through which the agent can decide whether to declare the statement true or false based on the probability and other knowledge that the agent has.

Dynamic Certainty for a Conclusion :

CF(C) = CF(H) x CF(R) , C is the conclusion

CF(H) = calculated Certainty Factor for Hypothesis.

R = given rule , "if H then C with certainty CF(R)"

Conjunction and Disjunction of Hypotheses:

CF (P1 and P2) = MIN { CF(P1) , CF(P2) }
CF (P1 or P2) = MAX { CF(P1) , CF(P2) }

Here, P1 , P2 are premises.

Given Rule is (P1 and P2) or P3

R1 and R2 are conclusions of rules.

CF(R1)= 0.8 

CF(R2)=0.3

CF(P1)=0.5  , CF(P2)=0.8 , CF(P3)=0.2

Where P1,P2,P3 are premises.

Now the CF of given rule ((P1 and P2) or P3)=   MAX  { MIN{CF(P1),CF(P2)} , CF(P3) } 

= MAX { MIN { 0.5,0.8} ,0.2}

=MAX { 0.5 , 0.2 }

= 0.5

Now, CF of R1 based on premises = 0.5 × CF(R1) =0.5 × 0.8 =0.40

Now, CF of R2 based on premises = 0.5 ×  CF(R2) =0.5 × 0.3 =0.15

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