Question 1

Functional dependency basics:
Evaluate the claim:

"The functional dependency R → S means that the value of S can be determined when we are given a value of R."

Accepted Answer

Introduction / Context: Functional dependencies (FDs) are central to relational theory and normalization. Understanding the precise meaning of R → S helps in designing schemas that minimize redundancy and anomalies (update, insertion, deletion). Given Data / Assumptions: R and S denote attribute sets in a relation schema. R → S reads “R functionally determines S.” We assume the standard definition used in normalization theory. Concept / Approach: By definition, R → S holds if, for any two tuples in the relation with the same values for all attributes of R, those tuples also have the same values for all attributes of S. In practical terms, knowing R fixes (determines) S. This is the basis for identifying keys (e.g., candidate keys where R determines all other attributes) and for decompositions that achieve normal forms (2NF, 3NF, BCNF). Step-by-Step Solution: Restate definition: if t1.R = t2.R then t1.S = t2.S for all tuples t1, t2. Interpretation: given a value of R, there is at most one corresponding S value in the relation. Examples: StudentID → StudentName; ISBN → Title; (Country, Code) → DialingPrefix. Verification / Alternative check: Test data for violations: if any two rows share R but disagree on S, the FD does not hold; this motivates constraints and normalization to enforce intended dependencies. Why Other Options Are Wrong: Incorrect: contradicts the formal definition. Only for one-to-one: FDs include one-to-many from keys to attributes (key determines many dependent attributes); “one-to-one” is not required. Only after normalization: FDs exist regardless of current normal form; normalization uses (not creates) them. Common Pitfalls: Confusing FDs with foreign-key relationships; assuming FDs depend on current data samples instead of the intended design rule over all possible tuples; thinking FDs require uniqueness both ways (they don’t—determination is directional). Final Answer: Correct

Question 2

Relational database theory — evaluate the claim about keys:
“A candidate key is a determinant that determines all the other columns in a relation.”
Decide whether this statement is correct or incorrect, keeping in mind the formal definition of can…

Accepted Answer

Introduction / Context: In the relational model, keys and functional dependencies describe how attributes (columns) relate. A candidate key is central to design because it uniquely identifies tuples (rows). This question asks whether a candidate key can be described as a determinant for all other columns in the relation. Given Data / Assumptions: We are working with one relation (table) R. “Determines” refers to functional determination: X → Y means attributes X functionally determine attributes Y. “Candidate key” means a minimal superkey: it functionally determines every attribute in R and has no proper subset with that property. Concept / Approach: A candidate key K is defined by two properties: uniqueness (K identifies each tuple) and minimality (no subset of K has the uniqueness property). Uniqueness in terms of dependencies means K → all attributes of R. Because K is minimal, removing any attribute from K breaks that determination. Therefore, calling a candidate key a “determinant of all other columns” is consistent with the formal definition. Step-by-Step Solution: Let K be a candidate key of relation R.By definition, K is a superkey ⇒ K → R (every attribute in R).By minimality, for any proper subset K', K' ↛ R.Hence K is indeed a determinant for all other attributes. Verification / Alternative check: Consider a Student(roll_no, name, program) relation where roll_no uniquely identifies each student. Then roll_no → name, program, and roll_no is minimal. This matches the claim precisely. Why Other Options Are Wrong: “Incorrect — confuses with foreign key”: foreign keys reference keys in another relation and do not determine all columns here. “Only sometimes correct”: the property holds regardless of whether the key is single-attribute or composite. “Not enough information” and “Irrelevant” ignore the standard definition of a candidate key. Common Pitfalls: Mixing up primary, candidate, and superkeys; forgetting the minimality part of the candidate key definition; assuming determination depends on normal form (it does not). Final Answer: Correct — this statement is valid for candidate keys

Question 3

Relational terminology check — are table rows ever called “fields”?
Decide whether the claim “In a relation, the rows are sometimes called fields” is correct or incorrect. Use standard database terminology: row/tuple vs column/attribute/field.

Accepted Answer

Introduction / Context: Clear terminology is essential in database design and SQL. Misnaming common concepts such as rows and columns leads to confusion in requirements, schema design, and query writing. This question tests whether calling rows “fields” is acceptable. Given Data / Assumptions: A “relation” is represented as a two-dimensional table. A row corresponds to a tuple (a single record instance). A column corresponds to an attribute; in many tools, a “field” commonly refers to a column or a single data item within a row/column intersection. Concept / Approach: In the relational model: rows = tuples (records), columns = attributes. The term “field” informally denotes either a column or, in some contexts, a single data item value at the intersection of a row and a column. It does not denote an entire row. Therefore, the statement that rows are fields is incorrect by standard usage. Step-by-Step Solution: Map relational terms: row → tuple/record.Column → attribute; “field” is commonly used as attribute or cell.Compare: “rows are fields” contradicts the mapping.Conclude the statement is incorrect. Verification / Alternative check: Look at SQL syntax: INSERT adds a row (tuple); ALTER TABLE ADD COLUMN adds an attribute (field). The operations and documentation consistently differentiate rows and columns. Why Other Options Are Wrong: “Correct — rows are fields” reverses the accepted definitions. “Correct only in hierarchical databases” and “Correct only in spreadsheets” are irrelevant; the question is about relations (relational model). “Depends on vendor” is misleading: while documentation wording varies, the accepted model does not equate rows with fields. Common Pitfalls: Using “field” for both a column and a cell; carrying spreadsheet vocabulary to relational database discourse. Final Answer: Incorrect — rows are tuples or records; fields are columns

Question 4

Normalization concept check — does Fifth Normal Form (5NF) isolate multivalued dependencies?
Evaluate the statement: “A relation is in 5NF when multivalued dependencies are isolated in their own relation.” Decide if this is correct or incorrect.

Accepted Answer

Introduction / Context: Normal forms help reduce redundancy and anomalies by constraining functional and other dependencies. The question distinguishes between Fourth Normal Form (4NF) and Fifth Normal Form (5NF, also called Project-Join Normal Form, PJ/NF). Given Data / Assumptions: We consider lossless decomposition and dependency preservation principles. Multivalued dependencies (MVDs) are a generalization of functional dependencies describing independent attribute sets. Join dependencies (JDs) extend the idea of constraints that require a relation to equal the join of its projections. Concept / Approach: 4NF eliminates nontrivial multivalued dependencies (X ↠ Y) that are not implied by a candidate key; to achieve 4NF, relations with problematic MVDs are decomposed so each independent multi-valued attribute set resides separately. 5NF goes further: it ensures that every nontrivial join dependency is implied by the candidate keys. In other words, 5NF eliminates redundancy arising only reconstructible through lossless joins of three or more projections, not merely those from MVDs. Step-by-Step Solution: Identify dependency type: statement claims 5NF targets MVDs.Recall definitions: 4NF ↔ removal of nontrivial MVDs; 5NF ↔ join dependencies.Therefore the claim misattributes MVD isolation to 5NF.Conclusion: the statement is incorrect. Verification / Alternative check: Standard texts: 3NF/BCNF handle FDs; 4NF handles MVDs; 5NF (PJ/NF) handles JDs where redundancy appears only when three or more tables are joined. Why Other Options Are Wrong: “Correct — 5NF addresses MVDs” is precisely the confusion. “Correct only when there are no candidate keys” and “Depends on the DBMS” are unrelated to the theoretical definitions. “3NF handles MVDs” is false; 3NF addresses FDs and transitive dependencies. Common Pitfalls: Equating MVDs with JDs; assuming higher normal forms always supersede earlier ones in a single dependency type. Final Answer: Incorrect — 4NF addresses multivalued dependencies; 5NF addresses join dependencies

Question 5

Key terminology — decide whether the following claim is correct:
“Keys made up of two or more attributes are called composite keys.” Provide the best evaluation.

Accepted Answer

Introduction / Context: Schema designers often need multiple attributes to uniquely identify a tuple. When more than one attribute participates jointly in uniqueness, the resulting key has a specific name and properties. Given Data / Assumptions: “Key” means a set of attributes that uniquely identifies rows (tuples). “Composite” implies more than one component attribute. We do not constrain whether the composite key is primary, alternate, or candidate. Concept / Approach: A composite (or compound) key is exactly a key composed of two or more attributes. It can be a candidate key or the chosen primary key. The definition does not depend on normal form, foreign keys, or whether the attributes are natural or surrogate. Step-by-Step Solution: Identify semantic content: “two or more attributes” → composite.Match with definition: composite key = multi-attribute key.No contradictory conditions are present.Therefore, the claim is correct. Verification / Alternative check: Example: Enrollment(student_id, course_id, term) might have primary key (student_id, course_id, term). This three-attribute primary key is composite. Why Other Options Are Wrong: Foreign key status is irrelevant to being composite. Surrogate keys are artificial identifiers and may be single-attribute; composite does not imply surrogate. Normal form level (BCNF, 3NF) is unrelated to the definition. Common Pitfalls: Confusing composite keys with concatenated values; assuming composite keys cannot be primary keys (they can); believing composite implies cross-table linkage (it does not). Final Answer: Correct — that is the definition of a composite key

Question 6

Functional dependency basics — evaluate the statement:
“A functional dependency is a relationship between or among attributes.” Decide whether this description is correct or incorrect.

Accepted Answer

Introduction / Context: Functional dependencies (FDs) formalize how some attributes determine others in a relation. They are foundational to normalization decisions (3NF, BCNF) and to reasoning about keys and redundancy. Given Data / Assumptions: An FD X → Y states that, for any two tuples in a relation, if they agree on X, they must agree on Y. X and Y are sets of attributes within the same relation schema. FDs are semantic constraints, not merely empirical correlations. Concept / Approach: Because both sides of an FD are attribute sets of the same relation, it is indeed a relationship among attributes (often between a determinant set and a dependent set). It does not represent an inter-relation constraint like referential integrity, nor does it require composite keys or foreign keys to exist. Step-by-Step Solution: Identify objects involved in an FD: attributes within one relation.Interpretation: agreement on X implies agreement on Y — a constraint among attributes.Therefore, the statement characterizes FDs correctly.No additional conditions (keys, foreign keys) are required. Verification / Alternative check: Example: In Student(roll_no, name), roll_no → name is an FD between attributes; if roll_no repeats, the name must repeat identically. Why Other Options Are Wrong: FDs are not inter-relation constraints; referential integrity handles cross-relation links. Composite keys are irrelevant to whether FDs exist. Sample data is not necessary; FDs reflect business rules and are defined over all possible instances. Common Pitfalls: Mistaking FDs for correlations in a sample; assuming FDs require keys; conflating FDs with foreign keys. Final Answer: Correct — it is a constraint relating attributes (determinant → dependent)

Question 7

Primary key vs. candidate key — evaluate the claim:
“A primary key is a composite key that was selected to be the main identifier for the relation.” Indicate whether this is correct or incorrect.

Accepted Answer

Introduction / Context: Keys are central to relational integrity. The primary key is the designated unique identifier, selected from the set of candidate keys. This question tests whether the primary key must necessarily be “composite.” Given Data / Assumptions: Candidate keys are minimal superkeys; there can be multiple candidate keys. The primary key is one selected candidate key to act as the main identifier. Candidate keys can be single-attribute or multi-attribute (composite). Concept / Approach: The definition of primary key does not require compositeness. A primary key can be a single attribute (e.g., roll_no) or a composite (e.g., order_id + line_no). What distinguishes a primary key from other candidate keys is the choice for identification and referential referencing, not the number of attributes. Step-by-Step Solution: Identify claim: “primary key is a composite key.”Compare with definition: primary key = chosen candidate key (may be single or composite).Therefore, the statement is incorrect.Implication: primary keys are about selection, not composition. Verification / Alternative check: Typical tables have single-attribute primary keys (e.g., id SERIAL/IDENTITY). Composite primary keys exist but are not mandatory. Why Other Options Are Wrong: “Correct — must be composite” contradicts widespread practice and definition. BCNF status and foreign keys are irrelevant to whether a primary key is composite. Primary keys are not necessarily surrogate; they can be natural keys. Common Pitfalls: Equating “primary” with “composite”; assuming surrogate keys are required. Final Answer: Incorrect — a primary key is a chosen candidate key and may be single-attribute

Question 8

Relational model overview — evaluate the statement:
“A relation is a two-dimensional table.” Decide whether this simplification is acceptable in database theory contexts.

Accepted Answer

Introduction / Context: The tabular representation is the most common mental model for relations. While a relation is formally a set of tuples over attributes, it is routinely visualized and manipulated as a two-dimensional table. Given Data / Assumptions: Rows correspond to tuples (records). Columns correspond to attributes (fields). Order of rows and columns in formal theory is not significant; duplicates are not allowed in a strict relation. Concept / Approach: Calling a relation a “two-dimensional table” is an acceptable pedagogical simplification used in textbooks and practice. The key is to remember the underlying set semantics: no duplicate tuples, attribute names define columns, and order does not matter. Within those caveats, the statement is considered correct for introductory and applied contexts. Step-by-Step Solution: Map formal concepts to table semantics.Recognize the 2D grid representation as conventional.Account for set-based nuances: ordering and duplicates are theoretical differences from many SQL systems.Conclude the statement is acceptable as a simplification. Verification / Alternative check: SQL DDL/DML operate on tables whose abstraction mirrors relations; relational algebra operations (selection, projection, join) align with tabular processing. Why Other Options Are Wrong: Relations are not graphs or multidimensional arrays by definition. The description is not limited to a specific DBMS; it is a model-level view. Common Pitfalls: Forgetting that a relation is a set (no duplicates) and that order is not defined; assuming SQL table behaviors (like duplicates) always match theory. Final Answer: Correct — a relation can be represented as a two-dimensional table of tuples and attributes

Question 9

Purpose of relations — evaluate the claim:
“The only reason for the existence of relations is to store instances of functional dependencies.” Decide whether this statement is correct or incorrect.

Accepted Answer

Introduction / Context: The relational model provides a logical structure for storing and querying data. Constraints such as functional dependencies help ensure data integrity, but they are not the sole purpose of relations. Given Data / Assumptions: Relations store facts about entities and their relationships. Functional dependencies (FDs) describe integrity constraints among attributes. Normalization uses FDs (and other dependencies) to guide schema decomposition. Concept / Approach: A relation’s existence is justified by the need to store and retrieve consistent, meaningful data. FDs are one class of constraints used to maintain correctness and reduce redundancy. Other constraints (keys, foreign keys, check constraints), semantics (business rules), and use cases (queries, analytics, transactions) also motivate relations. Therefore, asserting that relations exist only to store FD instances is incorrect and too narrow. Step-by-Step Solution: Identify the claim’s scope: “only reason … to store FDs.”Contrast with broader goals: represent entity sets and relationships; support efficient, correct querying.Recognize that FDs are constraints on data schemas, not the data’s primary purpose.Conclude the statement is incorrect. Verification / Alternative check: Consider a reporting database: tables exist primarily to store facts and dimensions; FDs inform keys and normalization but are not the reason those tables exist. Why Other Options Are Wrong: “Correct only after BCNF” is misguided; even in BCNF, relations serve to store application data. “Performance tuning” is not the purpose of relations, though design choices affect performance. “Not decidable” ignores the clear conceptual separation between data storage and dependency constraints. Common Pitfalls: Confusing constraints (FDs) with data content; overemphasizing normalization goals at the expense of modeling needs. Final Answer: Incorrect — relations exist to store data about entities and relationships; FDs are constraints

Question 10

Inference from functional dependencies — evaluate the claim:
“Given the dependency R → (S, T), it also follows that R → S.” Decide whether this inference is correct or incorrect.

Accepted Answer

Introduction / Context: Reasoning with functional dependencies relies on inference rules (Armstrong’s axioms and derived rules). One of the most used rules is decomposition (also called projectivity), which allows splitting a dependency with multiple attributes on the right-hand side. Given Data / Assumptions: We are given R → (S, T), where R, S, and T are attribute sets. We assume standard FD semantics and closure rules. No special assumptions about keys are needed. Concept / Approach: The decomposition rule states: If X → YZ then X → Y and X → Z. This follows from reflexivity and augmentation within Armstrong’s axioms. Intuitively, if R determines both S and T together, then R determines S individually (and T individually) as well. Step-by-Step Solution: Start with R → ST (Y = S, Z = T).Apply decomposition: derive R → S and R → T.Therefore, the claim R → S is valid.No key assumption is necessary for this inference. Verification / Alternative check: Compute attribute closure R+ given R → ST: S ∈ R+, T ∈ R+. Hence R → S is implied. Why Other Options Are Wrong: Requiring R → T first is unnecessary; both are simultaneous consequences. Key status is irrelevant to the inference. Sample data is not required; inference is rule-based. Common Pitfalls: Thinking combined determination is stronger than individual determination; forgetting standard FD inference rules. Final Answer: Correct — by the decomposition rule of functional dependencies

Question 11

BCNF criterion — evaluate the statement:
“A relation is in Boyce–Codd Normal Form (BCNF) if every determinant is a composite key.” Determine whether this is correct or incorrect.

Accepted Answer

Introduction / Context: Boyce–Codd Normal Form (BCNF) strengthens Third Normal Form by tightening the condition on determinants (the left-hand sides of functional dependencies). Understanding the exact requirement avoids incorrect schema validations. Given Data / Assumptions: Determinant means the attribute set on the left side of an FD X → Y. Candidate key means a minimal superkey — it functionally determines all attributes of the relation. Keys can be single-attribute or composite. Concept / Approach: The BCNF condition is: for every nontrivial FD X → Y that holds in the relation, X must be a superkey (equivalently, every determinant is a candidate key when minimal). There is no requirement that X be composite; a single-attribute key satisfies BCNF just as well. Therefore, saying “every determinant is a composite key” is too restrictive and incorrect. Step-by-Step Solution: State BCNF: ∀ FDs X → Y (nontrivial), X is a superkey.Note: superkey/candidate key may have one or many attributes.Compare with claim: insists determinants must be composite — extra, wrong condition.Conclude the statement is incorrect. Verification / Alternative check: Example: Relation R(A, B, C) with key A (single attribute). If the only FDs are A → B, A → C, then X = A is a determinant and a candidate key; R is in BCNF even though the determinant is not composite. Why Other Options Are Wrong: “Correct — determinants must be composite” contradicts the definition. Dependence on FD count or absence of single-attribute keys is irrelevant. BCNF is unrelated to foreign keys specifically. Common Pitfalls: Confusing “stronger than 3NF” with “requires composite keys”; thinking BCNF forbids single-attribute keys. Final Answer: Incorrect — BCNF requires every determinant to be a candidate key (composite or single-attribute)

Question 12

Relational database theory: any table that meets the formal definition of a relation is, by definition, in First Normal Form (1NF). Is this statement accurate?

Accepted Answer

Introduction / Context: First Normal Form (1NF) is the foundational requirement in relational database design. The statement claims that if a table meets the formal definition of a relation, then it is already in 1NF. Understanding what a “relation” means in the relational model clarifies why this is considered correct in most database textbooks and industry practice. Given Data / Assumptions: A “relation” refers to the formal construct of the relational model, not just any arbitrary spreadsheet-like table. Attributes (columns) are defined over domains of atomic (indivisible) values. There is no inherent ordering of rows (tuples) or columns (attributes), and there are no duplicate rows in a relation. Concept / Approach: 1NF requires that every attribute value is atomic (no repeating groups, no arrays in a single cell), and that the table behaves as a mathematical relation: unordered tuples, unordered attributes, and no duplicate tuples. The classical definition of a relation already encompasses atomic domains and these properties, so a structure that truly satisfies the definition of a relation is, by construction, in 1NF. Step-by-Step Solution: Identify the formal properties of a relation: no row order, no column order, no duplicate rows, and atomic values.Compare with 1NF requirements: atomic attribute values and tabular structure with one value per cell.Conclude that meeting the relation definition implies meeting 1NF constraints. Verification / Alternative check: Check any table: if a cell stores multiple values (e.g., comma-separated lists), it violates atomicity and thus fails to be a relation; therefore it also fails 1NF. Conversely, when all cells are single, indivisible values and other relation properties hold, it is considered 1NF. Why Other Options Are Wrong: “Incorrect” contradicts standard theory equating relations with 1NF tables. “Applies only to primary keys” misunderstands that 1NF applies to all attributes, not only keys. “Depends on referential integrity only” confuses normalization with relationship constraints between tables. Common Pitfalls: Confusing a spreadsheet or denormalized table (with lists in cells) for a relation. Also, thinking that 1NF concerns foreign keys; it does not—those are separate design concerns. Final Answer: Correct

Question 13

In a proper relation (relational model), does the physical or logical order of rows (tuples) matter for meaning or correctness?

Accepted Answer

Introduction / Context: The relational model treats a relation as a set of tuples. Sets do not have ordering. This question probes whether row order is semantically meaningful in a relation and whether changing row order could alter the meaning of the data. Given Data / Assumptions: A relation is a set (or multiset in implementations, but conceptually a set) of tuples. Set semantics imply no intrinsic ordering. Queries may apply ORDER BY to request a presentation order, but that is outside the relation’s definition. Concept / Approach: Because a relation is a set, any physical order on disk or logical order in memory is irrelevant to its meaning. SQL implementations often return rows in unspecified order unless ORDER BY is used. Therefore, the statement “the order of the rows matters” is false in the formal sense. Step-by-Step Solution: Recall: relations are sets; sets have no order.Recognize: any perceived order is a presentation artifact, not part of the schema or constraints.Conclude: changing row order does not change the relation’s meaning; hence the statement is incorrect. Verification / Alternative check: Run the same SELECT multiple times without ORDER BY; the database may choose different access paths and return different row orders. The data content remains the same, confirming order irrelevance. Why Other Options Are Wrong: “Correct” conflicts with the set-based definition. Mentions of indexes or exports are implementation details and do not make order semantically significant. Common Pitfalls: Assuming that because a clustered index stores rows in index key order, the relation “has” an order. That is storage, not logical meaning. Final Answer: Incorrect

Question 14

Functional dependency terminology: in the dependency R → S, is R properly called the determinant?

Accepted Answer

Introduction / Context: Functional dependencies (FDs) describe how attribute sets constrain each other. The left-hand side (LHS) of an FD determines the values on the right-hand side (RHS). This question checks your recall of the standard term for the LHS. Given Data / Assumptions: FD written as R → S, where R and S are sets of attributes. By definition, R functionally determines S. Minimality is not assumed unless specifically stated (e.g., candidate keys). Concept / Approach: The LHS of an FD is called the determinant because its values uniquely determine the RHS within a given relation state. Whether R is minimal (i.e., no proper subset determines S) is a separate property; minimal determinants correspond to candidate keys when RHS includes all attributes in the relation. Step-by-Step Solution: Interpret R → S as “for any two tuples agreeing on R, they must also agree on S.”Name the LHS (R) as the determinant because it determines S.Conclude that the statement is correct by standard terminology. Verification / Alternative check: Consult normalization steps (e.g., 3NF) where determinants play a role in identifying transitive dependencies. Why Other Options Are Wrong: “Incorrect” contradicts accepted terminology. Conditions about keys or minimality are unnecessary to name the determinant. Common Pitfalls: Confusing “determinant” with “candidate key.” A candidate key is a minimal determinant of all attributes in the relation. Final Answer: Correct

Question 15

Relational vocabulary: in a relation, are rows sometimes informally called “records” (even though the formal term is “tuples”)?

Accepted Answer

Introduction / Context: Different data models use different vocabulary. In the relational model, the formally correct term for a row is “tuple,” but many tools, textbooks, and practitioners informally say “record.” This question asks whether that informal usage is sometimes applied. Given Data / Assumptions: Relational theory uses “relation/attribute/tuple.” Legacy and practical usage often says “table/column/row/record.” The statement explicitly says “sometimes called ‘records’.” Concept / Approach: While “record” comes from earlier file systems and hierarchical models, it remains common in practice and documentation to refer to a relational row as a record. Therefore, the statement is acceptable as phrased (“sometimes called”). It would be imprecise to assert that “record” is the formal term, but acknowledging common usage is correct. Step-by-Step Solution: Identify formal term: tuple.Recognize common term: record or row in many tools and UIs.Conclude: the statement is correct as it qualifies “sometimes called.” Verification / Alternative check: Check database client software and UI labels; you will often see “records returned.” Why Other Options Are Wrong: “Incorrect” would deny the widespread informal usage acknowledged by the statement. References to other models (hierarchical or object) are not required; this is about terminology breadth. Common Pitfalls: Insisting that only textbook terminology is acceptable in all contexts; in exams, read qualifiers like “sometimes” carefully. Final Answer: Correct

Question 16

In a proper relation, does the order of the columns (attributes) matter for the meaning of the data?

Accepted Answer

Introduction / Context: Attributes in a relation form a set, and sets have no inherent order. This question checks whether column order is semantically meaningful in the relational model. Given Data / Assumptions: A relation is defined over a set of attributes, not an ordered list. SQL may display columns in a chosen order, but that is presentation. Users can request any explicit order in SELECT lists. Concept / Approach: Because attributes are a set, shuffling column positions does not change the relation’s meaning. Only attribute names and their domains matter. Any dependence on visual position is a UI concern, not a relational one. Step-by-Step Solution: Recall set semantics: sets are unordered.Relate to schema: schema defines attribute names and types, not positions.Conclude: column order does not matter to the relation’s semantics; thus the statement is correct. Verification / Alternative check: Write SELECT y, x FROM T; it is the same data as SELECT x, y FROM T reordered; the schema is unchanged. Why Other Options Are Wrong: “Incorrect” conflicts with fundamental set semantics. Mentions of SELECT * or composite keys are implementation/presentation issues, not changes to meaning. Common Pitfalls: Believing that position in CREATE TABLE determines semantics. It determines default display order only. Final Answer: Correct

Question 17

Does a proper relation allow cells that hold multi-valued entries (e.g., lists or arrays inside a single cell)?

Accepted Answer

Introduction / Context: First Normal Form (1NF) requires atomicity: each cell in a relation must contain a single, indivisible value from its attribute domain. The question asks whether multi-valued cells are permitted in a relation. Given Data / Assumptions: We are talking about the relational model, not semi-structured stores. Atomicity applies uniformly across all attributes. Examples of multi-valued entries include comma-separated lists, arrays, or nested tables stored in one cell. Concept / Approach: Multi-valued cells violate 1NF. In a properly normalized design, each value occupies its own row in a related table connected by keys. This ensures simpler constraints, predictable querying, and avoids update anomalies (insertion, deletion, and modification anomalies). Step-by-Step Solution: Check a cell containing “red, blue, green”.Observe that it encodes multiple values in one attribute cell.Conclude this is not atomic and therefore the table is not a proper relation in 1NF. Verification / Alternative check: Normalize by splitting multi-valued data into a child relation (e.g., ProductColor(product_id, color)). Each color then resides in its own tuple, satisfying atomicity. Why Other Options Are Wrong: “Correct” contradicts 1NF. Restrictions to non-key attributes or warehouse contexts do not change the relational definition; they are practical compromises, not theoretical allowances. Common Pitfalls: Storing CSV strings, arrays, or JSON blobs for convenience leads to difficult querying and constraint enforcement. Final Answer: Incorrect

Question 18

Are relations categorized into normal forms because of their referential integrity constraints (foreign-key relationships)?

Accepted Answer

Introduction / Context: Normalization organizes relations to reduce redundancy and prevent anomalies. This question checks whether normal forms are defined by referential integrity constraints (foreign keys) rather than by functional and multivalued dependencies. Given Data / Assumptions: Normal forms like 1NF, 2NF, 3NF, and BCNF are defined using keys and functional dependencies. Higher forms like 4NF involve multivalued dependencies; 5NF involves join dependencies. Referential integrity constraints ensure cross-table consistency but are not the basis for normal form definitions. Concept / Approach: 2NF removes partial dependency on a candidate key, 3NF removes transitive dependency on a candidate key, and BCNF strengthens the determinant requirement. None of these definitions depends on foreign keys. Foreign keys are orthogonal constraints ensuring that referenced values exist in parent tables. Step-by-Step Solution: Examine 2NF: no partial dependency of non-key attributes on part of a composite key.Examine 3NF: no transitive dependency of non-key attributes on a key.Examine BCNF: every determinant is a candidate key.Note: none of these rely on foreign key definitions. Verification / Alternative check: Look up formal definitions of normal forms—their statements reference dependencies, not referential integrity rules. Why Other Options Are Wrong: “Correct” confuses two distinct areas: normalization (dependencies) vs. referential integrity (relationships between tables). Mentions of BCNF or indexing foreign keys are irrelevant to the defining criteria. Common Pitfalls: Assuming that because normalization often introduces foreign keys, the forms are defined by them. The definitions remain dependency-based. Final Answer: Incorrect

Question 19

Is a functional dependency (FD) an equation, or is it a semantic constraint about attribute values?

Accepted Answer

Introduction / Context: Functional dependencies express how attribute sets determine other attribute sets. The question claims an FD is “always an equation,” which is a misunderstanding of its nature. Given Data / Assumptions: An FD is written X → Y, indicating that tuples agreeing on X must agree on Y. It is a logical/semantic constraint, not arithmetic or algebraic equality. FDs guide normalization (2NF, 3NF, BCNF). Concept / Approach: FDs are not equations; they do not equate values or expressions. They assert dependence: X values functionally determine Y values. While sometimes one writes Y = f(X) informally, that is a conceptual mapping, not an equation to be solved. Thus, calling FDs “equations” is incorrect. Step-by-Step Solution: Interpret X → Y: if two rows have the same X, they must have the same Y.Recognize this is a constraint about allowed states of the relation.Conclude the statement “FD is always an equation” is incorrect. Verification / Alternative check: Review Armstrong’s axioms (reflexivity, augmentation, transitivity) that manipulate dependencies, not arithmetic equalities. Why Other Options Are Wrong: References to normal forms or numeric keys do not convert constraints into equations. Common Pitfalls: Confusing functional dependence with mathematical functions in the algebraic sense and treating schema constraints as expressions to solve. Final Answer: Incorrect

Question 20

Key definition: is a key a set of one or more attributes that uniquely identifies a row (tuple) in a relation?

Accepted Answer

Introduction / Context: Keys are central to relational design. This question checks the basic definition of a key in a relation and whether it uniquely identifies tuples. Given Data / Assumptions: We consider candidate keys and the chosen primary key. A key can consist of one attribute or a combination (composite key). Uniqueness is the defining property; minimality defines a candidate key. Concept / Approach: A key is any minimal superkey: it uniquely identifies rows and no proper subset does. The statement focuses on uniqueness, which is correct. Whether a key is selected as the primary key is a naming and implementation choice; it does not affect the definition. Step-by-Step Solution: Recall: superkey = attributes whose combined values are unique across tuples.Key (candidate key) = minimal superkey.Therefore, a key does uniquely identify a row. Verification / Alternative check: In SQL, declare UNIQUE constraints (or PRIMARY KEY) to enforce uniqueness, reflecting the theoretical concept. Why Other Options Are Wrong: “Incorrect” contradicts the fundamental definition. Limiting to primary key is unnecessary; all candidate keys uniquely identify rows. Numeric types are irrelevant; keys can be strings, dates, etc. Common Pitfalls: Confusing “primary key” (one chosen key) with the general concept of “key” (candidate keys). Final Answer: Correct

The Relational Model and Normalization Questions