Q: Data removal vs. dropping objects: Evaluate the statement: "The SQL DELETE statement is used to delete both the table structure and the table data."

Introduction / Context: Understanding the differences among DELETE, TRUNCATE, and DROP is essential. DELETE removes rows; TRUNCATE quickly removes all rows; DROP removes the table object (structure) itself. This question asks whether DELETE removes both structure and data. Given Data / Assumptions: Standard SQL semantics for DML and DDL. No vendor-specific extensions that redefine DELETE. Concept / Approach: DELETE is a Data Manipulation Language (DML) operation that affects table contents (rows) and is typically transactionally logged, allowing rollback. The table definition persists. To remove structure, Data Definition Language (DDL) DROP TABLE is used. TRUNCATE TABLE is DDL (or DML-like in some vendors) that removes all rows more efficiently but still keeps the structure. Step-by-Step Solution: DELETE FROM t WHERE condition; — removes matching rows only.TRUNCATE TABLE t; — removes all rows, retains structure.DROP TABLE t; — removes the table object, indexes, and often dependent constraints (subject to CASCADE rules).Therefore the given statement is false for DELETE. Verification / Alternative check: After DELETE, DESCRIBE or SELECT * FROM t still works; after DROP TABLE, the object no longer exists and queries fail. Why Other Options Are Wrong: Valid only when CASCADE is specified: CASCADE applies to DROP/FOREIGN KEY relations, not DELETE removing structure. Temporary tables only: DELETE does not drop temporary tables either. Depends on isolation level: Isolation affects visibility and locking, not object persistence. Common Pitfalls: Confusing TRUNCATE with DROP; assuming DELETE without WHERE equals DROP (it does not—structure remains). Final Answer: Incorrect

Q: Where SQL can appear: Judge the statement: "SQL statements can be embedded in triggers, stored procedures, and program code."

Introduction / Context: SQL is used in multiple contexts: inside database-resident program units (procedures, functions, triggers) and in external application code via APIs such as JDBC, ODBC, ADO.NET, or embedded SQL preprocessors. This question verifies that you recognize these locations as valid places for SQL. Given Data / Assumptions: Typical enterprise DBMS capabilities are assumed (procedural languages and trigger support). Applications use drivers or ORMs to send SQL to the DBMS. Concept / Approach: Triggers and stored procedures are server-side constructs that execute in the DBMS process and can contain DML (SELECT/INSERT/UPDATE/DELETE) and often DDL (with permissions). Application code can issue SQL through parameterized commands, prepared statements, or ORM-generated SQL. Step-by-Step Solution: In triggers: respond to INSERT/UPDATE/DELETE/DDL events and run SQL to enforce rules/logging.In stored procedures: encapsulate business logic and data access; expose stable interfaces.In programs: use drivers to send SQL, fetch results, and commit/rollback transactions. Verification / Alternative check: Review vendor docs: PL/SQL (Oracle), T-SQL (SQL Server), PL/pgSQL (PostgreSQL) all allow embedded SQL; Java/Python/C# samples show SQL execution from code. Why Other Options Are Wrong: Incorrect: Conflicts with standard practice. Only SELECT: DML and even some DDL are common in these contexts. Only in ANSI/ISO DBMSs: This capability is widespread beyond strict standard scope. Dynamic but not static SQL: Both forms exist depending on language/tooling. Common Pitfalls: Believing that SQL belongs solely in applications and not in the database; overlooking governance benefits of procedures/triggers (centralized logic, permissions). Final Answer: Correct

Q: Counting data modification operations: Evaluate the claim: "There are three SQL data modification operations: insert, modify, and delete."

Introduction / Context: The classic SQL DML (Data Manipulation Language) operations are INSERT, UPDATE, and DELETE. The question uses the word “modify” to refer to UPDATE, which is the standard term. Understanding this trio is foundational to writing transactional applications. Given Data / Assumptions: Baseline SQL without considering vendor-specific statements like MERGE/UPSERT. Focus is on row-level changes to existing tables. Concept / Approach: INSERT adds new rows; UPDATE modifies existing rows; DELETE removes rows. Many vendors also implement MERGE (or INSERT ... ON CONFLICT/REPLACE variants) to combine insert/update logic, but these are built upon the same fundamental operations. Therefore, describing “three data modification operations” is pedagogically correct. Step-by-Step Solution: INSERT INTO t (...) VALUES (...); — create new rows.UPDATE t SET col = value WHERE ...; — change existing rows.DELETE FROM t WHERE ...; — remove rows.MERGE and UPSERT patterns conceptually orchestrate these basics. Verification / Alternative check: SQL documentation and reference cards universally present INSERT/UPDATE/DELETE as the primary DML trio. Why Other Options Are Wrong: Incorrect: Ignores the accepted trio. Only correct if MERGE is excluded: MERGE is an additional convenience; it does not negate the foundational three. OLTP vs. OLAP: DML applies in both contexts. Isolation level: Affects concurrency behavior, not the count of DML verbs. Common Pitfalls: Confusing TRUNCATE (bulk delete semantics) with the core DML triad; assuming MERGE is fundamental rather than syntactic sugar around inserts/updates/deletes. Final Answer: Correct

Q: Inserting through a view and NOT NULL requirements: Assess the statement: "If any required (NOT NULL) columns are missing from the view, the view cannot be used for inserting new data."

Introduction / Context: This question examines the rules for inserting rows through a view. When inserting via a view, the DBMS must be able to populate all required base-table columns. If the view does not expose some NOT NULL columns (without defaults), the DBMS has no values to insert, and the operation fails. Given Data / Assumptions: The view hides at least one base-table column defined as NOT NULL. No DEFAULT value or trigger exists to supply that missing value. We are inserting through the view, not directly into the base table. Concept / Approach: For an INSERT to succeed, every base-table column that is NOT NULL and lacks a DEFAULT must receive a value. If the column is not present in the view projection, the user cannot provide that value through the view, making the insert impossible unless other mechanisms exist (DEFAULTs, computed generated stored columns, INSTEAD OF trigger). Step-by-Step Solution: Identify base table NOT NULL columns with no default: these require explicit values.Check the view: if those columns are omitted, the INSERT statement through the view cannot set them.Result: the DBMS raises an error and rejects the insert. Verification / Alternative check: If DEFAULTs are defined or an INSTEAD OF trigger computes values, inserts can succeed. The question’s general statement (without such aids) holds true. Why Other Options Are Wrong: Incorrect: Ignores the requirement to supply NOT NULL columns. Valid only if defaults exist: This is actually a caveat where inserts may work; but the original statement correctly describes the failure when defaults are absent. Applies only to materialized views: The rule applies to ordinary views too. Type-specific: NOT NULL rules are independent of data type. Common Pitfalls: Forgetting to include required columns in a view intended for inserts; assuming DBMS will auto-populate them; overlooking DEFAULT constraints or triggers as remedies. Final Answer: Correct

Q: SQL Server trigger types: Judge the statement: "SQL Server does not support the BEFORE trigger."

Introduction / Context: Trigger timing varies by DBMS. Some platforms support BEFORE and AFTER triggers; others provide different semantics. Knowing which types exist in SQL Server is a frequent certification and interview topic. Given Data / Assumptions: We focus on Microsoft SQL Server T-SQL triggers. We include DML triggers on tables/views and INSTEAD OF triggers. Concept / Approach: SQL Server supports AFTER triggers (which fire after the DML statement completes but within the same transaction) and INSTEAD OF triggers (which replace the firing DML on a view or table). SQL Server does not implement a BEFORE trigger that executes prior to the DML statement while still allowing the statement to proceed automatically. Step-by-Step Solution: AFTER trigger: CREATE TRIGGER trg ON dbo.T AFTER INSERT AS ...INSTEAD OF trigger: CREATE TRIGGER trg ON dbo.V INSTEAD OF UPDATE AS ...No BEFORE keyword or equivalent timing exists. Verification / Alternative check: Compare with other DBMSs: Oracle and PostgreSQL support BEFORE row-level triggers; MySQL supports BEFORE triggers. The absence in SQL Server is well documented. Why Other Options Are Wrong: Incorrect: Conflicts with current T-SQL capabilities. Version/compatibility caveats: No version introduces true BEFORE triggers. Tables vs. views: Timing support does not change by object type in SQL Server. Common Pitfalls: Mistaking INSTEAD OF triggers for BEFORE triggers; they do not “pre-validate then proceed automatically”—they replace the operation and must perform the intended changes explicitly. Final Answer: Correct

Q: What is a stored procedure? Evaluate the statement: "A stored procedure is a program that performs some common action on database data and is stored in the database."

Introduction / Context: Stored procedures encapsulate database logic on the server side. They are central to many architectures for performance, security, and maintainability, enabling clients to call a stable, parameterized routine rather than issuing ad hoc SQL directly. Given Data / Assumptions: The definition concerns server-resident program units. We consider major DBMSs (SQL Server T-SQL procedures, Oracle PL/SQL procedures, PostgreSQL functions/procedures). Concept / Approach: A stored procedure is a named program stored in the database catalog that can execute SQL (DML/DDL) and procedural constructs (variables, loops, conditions, exceptions). Procedures can enforce business rules, reduce network round trips, and centralize permissions—clients may get EXECUTE rights without direct table access. Step-by-Step Solution: Create: CREATE PROCEDURE usp_close_order @id INT AS BEGIN ... END;Store: The definition/compiled plan is persisted in the database metadata.Execute: Applications call EXEC usp_close_order @id = 42;Effect: Procedure performs actions on data in a controlled, reusable way. Verification / Alternative check: Inspect metadata views (e.g., sys.procedures in SQL Server, USER_OBJECTS in Oracle) to see stored definitions; observe permissions and execution plans managed by the DBMS. Why Other Options Are Wrong: Incorrect: Contradicts standard DBMS definitions. Only SELECT: Procedures handle INSERT/UPDATE/DELETE and more. Only in Oracle: All major engines support stored program units. Compiled vs. interpreted: Implementation varies; the concept still matches the statement. Common Pitfalls: Confusing stored procedures with functions (which usually return a value and may have side-effect restrictions); assuming procedures are inherently faster—benefits often come from reduced network chatter and plan reuse, not magic speed. Final Answer: Correct

Q: SQL DDL knowledge check — confirm the command to define a view: “Is the SQL statement used to create a view named CREATE VIEW?” Choose the best evaluation.

Introduction / Context: Understanding core SQL Data Definition Language (DDL) verbs is foundational. This item checks whether you recognize the canonical syntax for defining a view. Views are named query definitions that present virtual tables to users and applications. Given Data / Assumptions: We refer to the standard SQL syntax for view creation. Vendor extensions (e.g., OR REPLACE, FORCE, WITH CHECK OPTION) exist but do not change the base verb. Privileges may affect execution but not the correctness of the statement name. Concept / Approach: The SQL statement to define a view is CREATE VIEW view_name AS SELECT .... This is consistent across SQL dialects, even if options differ. Therefore the assertion that the statement used is CREATE VIEW is correct. Variations like CREATE OR REPLACE VIEW or WITH CHECK OPTION refine behavior but do not alter the fundamental DDL verb. Step-by-Step Solution: Identify the required DDL verb for defining a view.Confirm the canonical syntax: CREATE VIEW ... AS SELECT ...Recognize optional clauses that may appear.Conclude the claim is correct. Verification / Alternative check: Consult any major vendor documentation; each provides CREATE VIEW with dialect-specific options (e.g., security barriers, schemabinding, column lists), but the verb remains CREATE VIEW. Why Other Options Are Wrong: “Incorrect” and “MAKE VIEW” contradict the standard. “Only correct with WITH CHECK OPTION” confuses a constraint clause with the DDL verb. “Indeterminate” is irrelevant; privileges do not rename the statement. Common Pitfalls: Confusing vendor options with the standard verb; assuming that inability to execute (due to permissions) changes the correctness of the statement name. Final Answer: Correct

Q: View definitions and GROUP BY — evaluate the statement: “Standard SQL-92 views are constructed from SELECT statements that do not contain the GROUP BY clause.” Decide if this is correct or incorrect.

Introduction / Context: Views encapsulate SELECT queries. The question asks whether SQL-92 prohibits GROUP BY in view definitions. Understanding the difference between “can define” versus “can update through” is essential: aggregation affects updatability, not definability. Given Data / Assumptions: We are discussing definitional capability (CREATE VIEW ... AS SELECT ...), not necessarily updatability. GROUP BY, HAVING, and aggregate functions may appear in SELECT statements. Vendors implement different updatability rules for aggregate views. Concept / Approach: SQL-92 allows views to be defined using arbitrary SELECT statements, which may include GROUP BY. However, views that perform aggregation are typically not inherently updatable because grouping destroys the one-to-one correspondence with base rows. Hence, the statement that standard views are constructed only from SELECT without GROUP BY is incorrect; GROUP BY is allowed but usually produces a non-updatable view. Step-by-Step Solution: Distinguish definition from updatability.Recognize that CREATE VIEW accepts SELECT queries with GROUP BY.Understand that aggregation limits DML pass-through.Conclude the statement is incorrect as written. Verification / Alternative check: Try CREATE VIEW v AS SELECT dept_id, COUNT(*) AS c FROM emp GROUP BY dept_id; — this is valid in mainstream SQL systems, though INSERT/UPDATE via v will be restricted. Why Other Options Are Wrong: Limiting GROUP BY to multi-table joins or claiming “undetermined” misunderstands the syntax rules. “Correct only for updatable views” mixes two separate topics; the stem asserts a blanket prohibition for standard views, which is false. Common Pitfalls: Confusing syntactic allowance with updatability; assuming anything not updatable is disallowed in a view definition. Final Answer: Incorrect

Question 1

Using views to expose calculations: Evaluate the statement:  "Views can be used to show the results of computed (derived) columns."

Accepted Answer

Introduction / Context: Views are virtual tables defined by queries. A common use case is to present derived or computed results—such as totals, formatted values, or conditional labels—without asking application code to repeat the same expressions everywhere. This question checks whether views can legitimately expose computed values to consumers. Given Data / Assumptions: A view is defined with a SELECT that includes expressions (e.g., price * qty AS line_total). No requirement is stated that the values be physically stored. Standard relational DBMS semantics apply. Concept / Approach: Because a view definition is a SELECT statement, any expression allowed in SELECT—arithmetic, string functions, CASE, date math, aggregates with GROUP BY (subject to updatability differences), and window functions—can be projected as a column in the view. Consumers query the view and receive the computed results as if they were columns, improving consistency and encapsulation. Step-by-Step Solution: Create a view: CREATE VIEW v_orders AS SELECT id, qty, price, qty * price AS amount FROM orders;Query the view: SELECT id, amount FROM v_orders;DBMS evaluates expressions at runtime and returns derived values without storing duplicates. Verification / Alternative check: Many systems also support materialized views that persist results; both standard (virtual) and materialized views can include computed expressions, though refresh strategies differ. Why Other Options Are Wrong: Incorrect: Contradicts what a SELECT can project. Only for materialized views: Virtual views handle expressions just fine. Only with window functions: Ordinary expressions are sufficient. Only in warehouses: Views are used across OLTP and OLAP systems. Common Pitfalls: Confusing display of computed values (supported) with updating them (often not supported). Assuming views must store results—virtual views compute on demand. Final Answer: Correct

Question 2

CHECK constraints in SQL: Judge the statement:  "Common types of SQL CHECK constraints include range checks and limiting column values to allowed sets."

Accepted Answer

Introduction / Context: CHECK constraints are declarative integrity rules enforced by the DBMS to restrict allowed values in a column or tuple. They are a key tool for maintaining data quality at the database level rather than relying solely on application code. Given Data / Assumptions: The statement references two common patterns: range checks and enumerated/domain checks. We assume a mainstream SQL DBMS supporting CHECK (e.g., PostgreSQL, SQL Server, Oracle, MySQL 8+ with CHECK enforced). Concept / Approach: A CHECK constraint evaluates a Boolean expression per row. Typical constraints include ranges (e.g., salary between 0 and 1e7), set membership (e.g., status in ('NEW','SHIPPED','CLOSED')), simple relational expressions (start_date <= end_date), and pattern checks (LIKE, basic functions). These are classic, widely used patterns for enforcing domain integrity. Step-by-Step Solution: Range example: CHECK (age >= 0 AND age <= 130)Domain example: CHECK (state IN ('CA','NY','TX'))Relational example: CHECK (start_date <= end_date)DBMS ensures any INSERT/UPDATE violating the expression is rejected. Verification / Alternative check: Inspect system catalogs to confirm constraints are stored and enforced; test with sample inserts to see violations blocked. Why Other Options Are Wrong: Incorrect: Disagrees with standard practice. Deferrable only: Many CHECKs are NOT DEFERRABLE; deferrability is optional. Only ANSI, not vendors: Major vendors support CHECK; behavior nuances exist but concept holds. Only numeric: Strings, dates, and other types work too. Common Pitfalls: Overusing triggers for validations that belong in CHECK; writing constraints that rely on subqueries (often disallowed in some DBMSs) instead of simpler per-row expressions. Final Answer: Correct

Question 3

Data removal vs. dropping objects: Evaluate the statement:  "The SQL DELETE statement is used to delete both the table structure and the table data."

Accepted Answer

Introduction / Context: Understanding the differences among DELETE, TRUNCATE, and DROP is essential. DELETE removes rows; TRUNCATE quickly removes all rows; DROP removes the table object (structure) itself. This question asks whether DELETE removes both structure and data. Given Data / Assumptions: Standard SQL semantics for DML and DDL. No vendor-specific extensions that redefine DELETE. Concept / Approach: DELETE is a Data Manipulation Language (DML) operation that affects table contents (rows) and is typically transactionally logged, allowing rollback. The table definition persists. To remove structure, Data Definition Language (DDL) DROP TABLE is used. TRUNCATE TABLE is DDL (or DML-like in some vendors) that removes all rows more efficiently but still keeps the structure. Step-by-Step Solution: DELETE FROM t WHERE condition; — removes matching rows only.TRUNCATE TABLE t; — removes all rows, retains structure.DROP TABLE t; — removes the table object, indexes, and often dependent constraints (subject to CASCADE rules).Therefore the given statement is false for DELETE. Verification / Alternative check: After DELETE, DESCRIBE or SELECT * FROM t still works; after DROP TABLE, the object no longer exists and queries fail. Why Other Options Are Wrong: Valid only when CASCADE is specified: CASCADE applies to DROP/FOREIGN KEY relations, not DELETE removing structure. Temporary tables only: DELETE does not drop temporary tables either. Depends on isolation level: Isolation affects visibility and locking, not object persistence. Common Pitfalls: Confusing TRUNCATE with DROP; assuming DELETE without WHERE equals DROP (it does not—structure remains). Final Answer: Incorrect

Question 4

Where SQL can appear: Judge the statement:  "SQL statements can be embedded in triggers, stored procedures, and program code."

Accepted Answer

Introduction / Context: SQL is used in multiple contexts: inside database-resident program units (procedures, functions, triggers) and in external application code via APIs such as JDBC, ODBC, ADO.NET, or embedded SQL preprocessors. This question verifies that you recognize these locations as valid places for SQL. Given Data / Assumptions: Typical enterprise DBMS capabilities are assumed (procedural languages and trigger support). Applications use drivers or ORMs to send SQL to the DBMS. Concept / Approach: Triggers and stored procedures are server-side constructs that execute in the DBMS process and can contain DML (SELECT/INSERT/UPDATE/DELETE) and often DDL (with permissions). Application code can issue SQL through parameterized commands, prepared statements, or ORM-generated SQL. Step-by-Step Solution: In triggers: respond to INSERT/UPDATE/DELETE/DDL events and run SQL to enforce rules/logging.In stored procedures: encapsulate business logic and data access; expose stable interfaces.In programs: use drivers to send SQL, fetch results, and commit/rollback transactions. Verification / Alternative check: Review vendor docs: PL/SQL (Oracle), T-SQL (SQL Server), PL/pgSQL (PostgreSQL) all allow embedded SQL; Java/Python/C# samples show SQL execution from code. Why Other Options Are Wrong: Incorrect: Conflicts with standard practice. Only SELECT: DML and even some DDL are common in these contexts. Only in ANSI/ISO DBMSs: This capability is widespread beyond strict standard scope. Dynamic but not static SQL: Both forms exist depending on language/tooling. Common Pitfalls: Believing that SQL belongs solely in applications and not in the database; overlooking governance benefits of procedures/triggers (centralized logic, permissions). Final Answer: Correct

Question 5

Counting data modification operations: Evaluate the claim:  "There are three SQL data modification operations: insert, modify, and delete."

Accepted Answer

Introduction / Context: The classic SQL DML (Data Manipulation Language) operations are INSERT, UPDATE, and DELETE. The question uses the word “modify” to refer to UPDATE, which is the standard term. Understanding this trio is foundational to writing transactional applications. Given Data / Assumptions: Baseline SQL without considering vendor-specific statements like MERGE/UPSERT. Focus is on row-level changes to existing tables. Concept / Approach: INSERT adds new rows; UPDATE modifies existing rows; DELETE removes rows. Many vendors also implement MERGE (or INSERT ... ON CONFLICT/REPLACE variants) to combine insert/update logic, but these are built upon the same fundamental operations. Therefore, describing “three data modification operations” is pedagogically correct. Step-by-Step Solution: INSERT INTO t (...) VALUES (...); — create new rows.UPDATE t SET col = value WHERE ...; — change existing rows.DELETE FROM t WHERE ...; — remove rows.MERGE and UPSERT patterns conceptually orchestrate these basics. Verification / Alternative check: SQL documentation and reference cards universally present INSERT/UPDATE/DELETE as the primary DML trio. Why Other Options Are Wrong: Incorrect: Ignores the accepted trio. Only correct if MERGE is excluded: MERGE is an additional convenience; it does not negate the foundational three. OLTP vs. OLAP: DML applies in both contexts. Isolation level: Affects concurrency behavior, not the count of DML verbs. Common Pitfalls: Confusing TRUNCATE (bulk delete semantics) with the core DML triad; assuming MERGE is fundamental rather than syntactic sugar around inserts/updates/deletes. Final Answer: Correct

Question 6

Inserting through a view and NOT NULL requirements: Assess the statement:  "If any required (NOT NULL) columns are missing from the view, the view cannot be used for inserting new data."

Accepted Answer

Introduction / Context: This question examines the rules for inserting rows through a view. When inserting via a view, the DBMS must be able to populate all required base-table columns. If the view does not expose some NOT NULL columns (without defaults), the DBMS has no values to insert, and the operation fails. Given Data / Assumptions: The view hides at least one base-table column defined as NOT NULL. No DEFAULT value or trigger exists to supply that missing value. We are inserting through the view, not directly into the base table. Concept / Approach: For an INSERT to succeed, every base-table column that is NOT NULL and lacks a DEFAULT must receive a value. If the column is not present in the view projection, the user cannot provide that value through the view, making the insert impossible unless other mechanisms exist (DEFAULTs, computed generated stored columns, INSTEAD OF trigger). Step-by-Step Solution: Identify base table NOT NULL columns with no default: these require explicit values.Check the view: if those columns are omitted, the INSERT statement through the view cannot set them.Result: the DBMS raises an error and rejects the insert. Verification / Alternative check: If DEFAULTs are defined or an INSTEAD OF trigger computes values, inserts can succeed. The question’s general statement (without such aids) holds true. Why Other Options Are Wrong: Incorrect: Ignores the requirement to supply NOT NULL columns. Valid only if defaults exist: This is actually a caveat where inserts may work; but the original statement correctly describes the failure when defaults are absent. Applies only to materialized views: The rule applies to ordinary views too. Type-specific: NOT NULL rules are independent of data type. Common Pitfalls: Forgetting to include required columns in a view intended for inserts; assuming DBMS will auto-populate them; overlooking DEFAULT constraints or triggers as remedies. Final Answer: Correct

Question 7

What is a trigger? Evaluate the statement:  "A trigger is a stored program that is attached to a database."

Accepted Answer

Introduction / Context: Triggers are special stored program units that execute automatically in response to specific database events. The precise attachment point matters: in most DBMSs, DML triggers are attached to a table or view, while some systems also support database- or server-level DDL/event triggers. The statement here is overly vague and misleading. Given Data / Assumptions: We consider common DBMSs (SQL Server, Oracle, PostgreSQL, MySQL). The statement does not limit itself to DML vs. DDL triggers. Concept / Approach: The standard definition associates DML triggers with a specific table or view: they fire on INSERT, UPDATE, or DELETE to that object. Some systems also provide DDL triggers that fire on CREATE/ALTER/DROP events at the database or schema scope. Therefore, saying a trigger is “attached to a database” incorrectly suggests that is the normal attachment for all triggers, which is not the case. Step-by-Step Solution: Typical: CREATE TRIGGER t_ai ON table_name AFTER INSERT ... — attached to a table/view.Less common but supported: CREATE TRIGGER ddl_db_trg ON DATABASE ... — database-scope DDL events (vendor-specific).Conclusion: The generalized statement is inaccurate; the usual attachment is to a table/view, not the database. Verification / Alternative check: Check vendor docs: SQL Server distinguishes DML triggers (on tables/views) and DDL triggers (on DATABASE/ALL SERVER). PostgreSQL uses triggers on tables; event triggers exist for DDL at database level. Why Other Options Are Wrong: Correct: Overly broad; misses the table/view attachment requirement for DML triggers. Valid only for DDL triggers: While this caveat can sometimes be true, the original statement is still misleading as a definition. Vendor-specific claims: Both Oracle and PostgreSQL primarily attach DML triggers to tables/views; database-level event triggers are a separate feature. Common Pitfalls: Defining triggers at the wrong scope; assuming a trigger can “watch everything” in a database by default; forgetting that triggers execute with transaction semantics tied to the firing statement. Final Answer: Incorrect

Question 8

SQL Server trigger types: Judge the statement:  "SQL Server does not support the BEFORE trigger."

Accepted Answer

Introduction / Context: Trigger timing varies by DBMS. Some platforms support BEFORE and AFTER triggers; others provide different semantics. Knowing which types exist in SQL Server is a frequent certification and interview topic. Given Data / Assumptions: We focus on Microsoft SQL Server T-SQL triggers. We include DML triggers on tables/views and INSTEAD OF triggers. Concept / Approach: SQL Server supports AFTER triggers (which fire after the DML statement completes but within the same transaction) and INSTEAD OF triggers (which replace the firing DML on a view or table). SQL Server does not implement a BEFORE trigger that executes prior to the DML statement while still allowing the statement to proceed automatically. Step-by-Step Solution: AFTER trigger: CREATE TRIGGER trg ON dbo.T AFTER INSERT AS ...INSTEAD OF trigger: CREATE TRIGGER trg ON dbo.V INSTEAD OF UPDATE AS ...No BEFORE keyword or equivalent timing exists. Verification / Alternative check: Compare with other DBMSs: Oracle and PostgreSQL support BEFORE row-level triggers; MySQL supports BEFORE triggers. The absence in SQL Server is well documented. Why Other Options Are Wrong: Incorrect: Conflicts with current T-SQL capabilities. Version/compatibility caveats: No version introduces true BEFORE triggers. Tables vs. views: Timing support does not change by object type in SQL Server. Common Pitfalls: Mistaking INSTEAD OF triggers for BEFORE triggers; they do not “pre-validate then proceed automatically”—they replace the operation and must perform the intended changes explicitly. Final Answer: Correct

Question 9

What is a stored procedure? Evaluate the statement:  "A stored procedure is a program that performs some common action on database data and is stored in the database."

Accepted Answer

Introduction / Context: Stored procedures encapsulate database logic on the server side. They are central to many architectures for performance, security, and maintainability, enabling clients to call a stable, parameterized routine rather than issuing ad hoc SQL directly. Given Data / Assumptions: The definition concerns server-resident program units. We consider major DBMSs (SQL Server T-SQL procedures, Oracle PL/SQL procedures, PostgreSQL functions/procedures). Concept / Approach: A stored procedure is a named program stored in the database catalog that can execute SQL (DML/DDL) and procedural constructs (variables, loops, conditions, exceptions). Procedures can enforce business rules, reduce network round trips, and centralize permissions—clients may get EXECUTE rights without direct table access. Step-by-Step Solution: Create: CREATE PROCEDURE usp_close_order @id INT AS BEGIN ... END;Store: The definition/compiled plan is persisted in the database metadata.Execute: Applications call EXEC usp_close_order @id = 42;Effect: Procedure performs actions on data in a controlled, reusable way. Verification / Alternative check: Inspect metadata views (e.g., sys.procedures in SQL Server, USER_OBJECTS in Oracle) to see stored definitions; observe permissions and execution plans managed by the DBMS. Why Other Options Are Wrong: Incorrect: Contradicts standard DBMS definitions. Only SELECT: Procedures handle INSERT/UPDATE/DELETE and more. Only in Oracle: All major engines support stored program units. Compiled vs. interpreted: Implementation varies; the concept still matches the statement. Common Pitfalls: Confusing stored procedures with functions (which usually return a value and may have side-effect restrictions); assuming procedures are inherently faster—benefits often come from reduced network chatter and plan reuse, not magic speed. Final Answer: Correct

Question 10

SQL integrity constraints — evaluate the claim about CHECK: “The SQL CHECK constraint is fully defined by the SQL-92 standard, and it is implemented consistently by all database vendors.” State whether this statement is correct or incorrect, conside…

Accepted Answer

Introduction / Context: CHECK constraints are part of SQL’s declarative integrity features that let designers restrict permissible values in a column or row. The prompt asserts that SQL-92 fully specified CHECK, and that implementations are consistent across vendors. This question tests your understanding of the standard versus actual DBMS support and portability concerns for constraints. Given Data / Assumptions: We consider the SQL-92 standard definition of CHECK constraints. “Implemented consistently” means behavior is functionally equivalent across major vendors (syntax, enforcement timing, scope, and edge cases). No vendor-specific extensions are assumed unless needed to explain inconsistency. Concept / Approach: While SQL-92 defines CHECK at the column and table level, vendors historically diverged in support. Some accepted definitions but ignored enforcement or postponed it; others limited expressions, subqueries, or user-defined functions; and naming, deferrability, and error messaging vary. Therefore, the idea that CHECK is consistently implemented is not accurate in practice. Standards define behavior; implementations can, and often do, lag or differ. Step-by-Step Solution: Identify the standard: SQL-92 includes CHECK constraints.Compare vendors historically and currently: differences exist (expression support, enforcement timing, deferrable behavior, error codes).Determine consistency claim: it fails due to observable cross-DBMS variance.Conclude: the statement is incorrect. Verification / Alternative check: Consider engines that long accepted CHECK syntax but did not enforce it, or engines that restrict expressions in CHECK. Porting a schema with complex CHECK logic often requires vendor-specific rewrites or tests, proving inconsistency. Why Other Options Are Wrong: “Correct” ignores practical divergences in enforcement and features. “Applies only to primary keys” is irrelevant; CHECK is orthogonal to keys. “Cannot be determined” is incorrect; industry experience clearly shows differences. “Embedded SQL only” confuses execution context with feature support. Common Pitfalls: Assuming that presence in the standard guarantees uniform implementation; overlooking vendor-specific restrictions; forgetting to test CHECK behavior when migrating between DBMS products. Final Answer: Incorrect

Question 11

Database triggers — evaluate the classification: “Three types of triggers are BEFORE, AFTER, and EXCEPTION.” State whether this statement is correct or incorrect, considering common SQL implementations and standard terminology (including INSTEAD OF …

Accepted Answer

Introduction / Context: Triggers are server-side routines that fire in response to data changes. Understanding their types and timing is essential for data integrity, auditing, and automation. The prompt suggests that the three types are BEFORE, AFTER, and EXCEPTION. This question checks whether that classification aligns with standard and widely used vendor terminology. Given Data / Assumptions: We compare common relational systems: Oracle, PostgreSQL, SQL Server, MySQL/MariaDB, etc. We consider the canonical timing categories for DML triggers. We acknowledge vendor-specific features such as INSTEAD OF for view updates. Concept / Approach: Standard terminology distinguishes triggers by timing relative to the triggering DML event: BEFORE and AFTER. Many vendors additionally support INSTEAD OF triggers (especially for updatable views), which replace the action rather than firing before or after it. “EXCEPTION” is not a standard timing type for triggers; exception handling is usually done within procedural code blocks, not as a trigger category. Thus the proposed trio is inaccurate. Step-by-Step Solution: List accepted types: BEFORE, AFTER, and often INSTEAD OF.Evaluate “EXCEPTION”: not a timing category; it is error/exception handling logic.Therefore, the claim’s classification is incorrect.Conclude that the correct set is BEFORE/AFTER (and possibly INSTEAD OF), not EXCEPTION. Verification / Alternative check: Review vendor docs: you find BEFORE/AFTER universally; EXCEPTION does not appear as a trigger kind. Some RDBMS provide statement-level versus row-level options, but those are orthogonal to timing types. Why Other Options Are Wrong: “Correct” conflicts with established nomenclature. “Depends on isolation level” confuses concurrency with trigger timing. “Mainframe dialects” and “procedural languages” are red herrings regarding SQL triggers. Common Pitfalls: Mixing exception blocks with trigger timing; forgetting INSTEAD OF triggers for view updates; assuming vendor-specific features are universal. Final Answer: Incorrect

Question 12

Views and data storage — evaluate the assertion: “An updatable view has its own data.” Decide whether this statement is correct or incorrect, distinguishing virtual views from materialized views.

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Introduction / Context: SQL views are commonly used to encapsulate queries, simplify security, and present tailored schemas to applications. The statement claims that an updatable view “has its own data,” which probes understanding of virtual versus materialized storage and how updates are propagated. Given Data / Assumptions: A standard (virtual) view is defined by CREATE VIEW AS SELECT ... Updatability depends on query properties (e.g., single base table, key preservation) and vendor rules. Materialized views (or indexed views) exist in some systems but are distinct from ordinary views. Concept / Approach: Virtual views do not store their own data; they present results computed from base tables at query time. When a virtual view is updatable, DML statements on the view are translated into DML on the underlying tables (sometimes with INSTEAD OF triggers). Only materialized views persist physical data, but even then the source of truth is the base tables. Therefore, saying an “updatable view has its own data” is incorrect in the ordinary sense of SQL views. Step-by-Step Solution: Differentiate view types: virtual vs materialized.Assess updatability rules: view DML maps to base tables; the view itself holds no independent tuples.Conclude: the claim is incorrect for standard updatable (virtual) views.Note: materialized/Indexed views persist data but remain derived from base tables. Verification / Alternative check: Execute INSERT/UPDATE on a simple updatable view; observe changes in the base table. Dropping the base table invalidates the view, revealing that the view has no independent data store. Why Other Options Are Wrong: “Correct” misstates how views work. “Depends on primary key/trigger/clustered index” mixes factors that affect updatability or performance, not whether a view stores data. Common Pitfalls: Confusing materialized views with ordinary views; assuming that because a view is updatable it must retain its own copy of data. Final Answer: Incorrect

Question 13

SQL DDL knowledge check — confirm the command to define a view: “Is the SQL statement used to create a view named CREATE VIEW?” Choose the best evaluation.

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Introduction / Context: Understanding core SQL Data Definition Language (DDL) verbs is foundational. This item checks whether you recognize the canonical syntax for defining a view. Views are named query definitions that present virtual tables to users and applications. Given Data / Assumptions: We refer to the standard SQL syntax for view creation. Vendor extensions (e.g., OR REPLACE, FORCE, WITH CHECK OPTION) exist but do not change the base verb. Privileges may affect execution but not the correctness of the statement name. Concept / Approach: The SQL statement to define a view is CREATE VIEW view_name AS SELECT .... This is consistent across SQL dialects, even if options differ. Therefore the assertion that the statement used is CREATE VIEW is correct. Variations like CREATE OR REPLACE VIEW or WITH CHECK OPTION refine behavior but do not alter the fundamental DDL verb. Step-by-Step Solution: Identify the required DDL verb for defining a view.Confirm the canonical syntax: CREATE VIEW ... AS SELECT ...Recognize optional clauses that may appear.Conclude the claim is correct. Verification / Alternative check: Consult any major vendor documentation; each provides CREATE VIEW with dialect-specific options (e.g., security barriers, schemabinding, column lists), but the verb remains CREATE VIEW. Why Other Options Are Wrong: “Incorrect” and “MAKE VIEW” contradict the standard. “Only correct with WITH CHECK OPTION” confuses a constraint clause with the DDL verb. “Indeterminate” is irrelevant; privileges do not rename the statement. Common Pitfalls: Confusing vendor options with the standard verb; assuming that inability to execute (due to permissions) changes the correctness of the statement name. Final Answer: Correct

Question 14

View definitions and GROUP BY — evaluate the statement: “Standard SQL-92 views are constructed from SELECT statements that do not contain the GROUP BY clause.” Decide if this is correct or incorrect.

Accepted Answer

Introduction / Context: Views encapsulate SELECT queries. The question asks whether SQL-92 prohibits GROUP BY in view definitions. Understanding the difference between “can define” versus “can update through” is essential: aggregation affects updatability, not definability. Given Data / Assumptions: We are discussing definitional capability (CREATE VIEW ... AS SELECT ...), not necessarily updatability. GROUP BY, HAVING, and aggregate functions may appear in SELECT statements. Vendors implement different updatability rules for aggregate views. Concept / Approach: SQL-92 allows views to be defined using arbitrary SELECT statements, which may include GROUP BY. However, views that perform aggregation are typically not inherently updatable because grouping destroys the one-to-one correspondence with base rows. Hence, the statement that standard views are constructed only from SELECT without GROUP BY is incorrect; GROUP BY is allowed but usually produces a non-updatable view. Step-by-Step Solution: Distinguish definition from updatability.Recognize that CREATE VIEW accepts SELECT queries with GROUP BY.Understand that aggregation limits DML pass-through.Conclude the statement is incorrect as written. Verification / Alternative check: Try CREATE VIEW v AS SELECT dept_id, COUNT(*) AS c FROM emp GROUP BY dept_id; — this is valid in mainstream SQL systems, though INSERT/UPDATE via v will be restricted. Why Other Options Are Wrong: Limiting GROUP BY to multi-table joins or claiming “undetermined” misunderstands the syntax rules. “Correct only for updatable views” mixes two separate topics; the stem asserts a blanket prohibition for standard views, which is false. Common Pitfalls: Confusing syntactic allowance with updatability; assuming anything not updatable is disallowed in a view definition. Final Answer: Incorrect

Question 15

Referential integrity nuance — evaluate the claim: “When a foreign-key column exists without an explicit FOREIGN KEY constraint, a ‘casual relationship’ between the two tables has been created.” Choose whether this is correct or incorrect.

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Introduction / Context: Foreign keys enforce referential integrity (RI) by constraining values in the child table to those existing in the parent. The statement suggests that merely having a column intended to reference another table creates a “casual relationship” without a formal constraint. This probes understanding of logical design versus enforced integrity. Given Data / Assumptions: A column named, for example, customer_id exists in an orders table. No FOREIGN KEY constraint has been defined. Application code may still treat the column as a reference by convention. Concept / Approach: Without an explicit FOREIGN KEY constraint, the database engine will not enforce RI. The link is at best conventional or “soft,” relying on application discipline. Such conventions are brittle: orphan rows can appear, deletes in the parent can leave dangling references, and updates may violate intended links. Therefore, the statement that a relationship has been “created” is incorrect; only a column has been created. A relationship, in the relational sense, requires an enforced constraint or a join condition used in queries—preferably both. Step-by-Step Solution: Check presence of the constraint: absent.Assess enforcement: none by the DBMS; integrity depends on external logic.Determine existence of relationship: not guaranteed; therefore the claim is incorrect.Conclude that you must define FOREIGN KEY ... REFERENCES ... to establish RI. Verification / Alternative check: Attempt to insert a child row with a non-existent parent key; without the constraint, the DBMS accepts it, proving the “relationship” is not enforced. Why Other Options Are Wrong: Matching names or isolation levels do not create constraints. “Indeterminate” is wrong—behavior is clear and testable. Common Pitfalls: Relying on naming conventions instead of constraints; believing ORMs automatically enforce RI at the server without explicit DDL. Final Answer: Incorrect

Question 16

Core DDL terminology — validate the command name: “The SQL MAKE TABLE command is used to construct tables, define columns, define column constraints, and create relationships.” Indicate whether this is correct or incorrect.

Accepted Answer

Introduction / Context: Knowing the proper DDL verbs is critical for database design and administration. The statement claims that “MAKE TABLE” is the SQL command for creating tables and constraints. This item tests your familiarity with standard SQL syntax versus colloquial phrasing. Given Data / Assumptions: We reference ISO/IEC SQL standards and mainstream RDBMS dialects. We include creating columns, constraints, and relationships in a table definition. We are not discussing GUI tools that may use buttons labeled “Make Table.” Concept / Approach: The correct DDL to create a table is CREATE TABLE, not MAKE TABLE. Within CREATE TABLE, you define columns, primary keys, unique constraints, foreign keys, checks, and defaults. Relationships (referential constraints) are specified using FOREIGN KEY ... REFERENCES ... clauses in the same statement or via ALTER TABLE. Therefore, the wording “MAKE TABLE” is incorrect for standard SQL. Step-by-Step Solution: Identify the standard verb: CREATE TABLE.Enumerate what CREATE TABLE can specify: columns and constraints.Compare with the claim: “MAKE TABLE” — not a standard SQL command.Conclude the statement is incorrect. Verification / Alternative check: Running “MAKE TABLE ...” in any mainstream SQL engine yields a syntax error. GUI tools may offer a “Make Table” wizard, but they ultimately emit CREATE TABLE DDL. Why Other Options Are Wrong: “Correct” and “SQL-89” are false; CREATE TABLE predates SQL-92. “Client tools” do not redefine SQL grammar; they generate standard DDL. Storage engines do not change the verb name. Common Pitfalls: Confusing UI labels or Access “Make-Table” queries with server SQL; assuming colloquial phrases are valid syntax. Final Answer: Incorrect

Question 17

Definition of a SQL view — evaluate the statement: “A SQL view is a virtual table constructed from other tables or views.” Choose the best evaluation.

Accepted Answer

Introduction / Context: Views are pervasive in database development, used for security, abstraction, and reuse. This question asks whether a view can be described as a virtual table built from other tables or views. It checks your grasp of the conceptual model rather than performance specifics. Given Data / Assumptions: We discuss standard virtual views created with CREATE VIEW AS SELECT ... Materialized or indexed views are a separate optimization feature in some systems. The phrase “constructed from other tables or views” refers to the underlying SELECT query. Concept / Approach: A view presents the result of a stored query as if it were a table. The engine evaluates the view definition (often by query rewrite) against base tables or even other views. Thus, describing a view as a “virtual table constructed from other tables or views” is accurate. Whether it is updatable or materialized does not change that fundamental definition; those are properties or implementations layered on top. Step-by-Step Solution: Identify view creation: CREATE VIEW v AS SELECT ... FROM base1 JOIN base2 ...Interpret “virtual table”: projection of a query’s columns exposed as table-like schema.Conclude: statement correctly characterizes what a view is.Note: materialization is optional and vendor-dependent. Verification / Alternative check: Querying a view with SELECT * FROM v behaves like querying a table; dropping referenced tables invalidates the view, showing its dependence on other objects. Why Other Options Are Wrong: Updatability and materialization are orthogonal to being a view. Execution plans vary but do not redefine what a view is. Common Pitfalls: Believing views always store data; conflating materialized views with standard views; assuming views must be updatable. Final Answer: Correct

Question 18

JOIN syntax knowledge — evaluate the claim: “SQL has a JOIN ... ON syntax that can be used to create inner joins and outer joins.” Decide whether this statement is correct or incorrect.

Accepted Answer

Introduction / Context: Explicit JOIN syntax was standardized to make join intent clear and avoid ambiguous WHERE-based joins. The claim is that SQL provides JOIN ... ON syntax for inner and outer joins. This tests your familiarity with ANSI/ISO join notation introduced in SQL-92 and widely adopted. Given Data / Assumptions: We use standard SQL keywords: INNER JOIN, LEFT/RIGHT/FULL OUTER JOIN, and ON for join predicates. Vendors may support additional syntaxes (e.g., USING) but they do not negate JOIN ... ON. We are not considering legacy proprietary outer-join operators. Concept / Approach: SQL supports both inner and outer joins using the JOIN ... ON form: SELECT ... FROM A INNER JOIN B ON A.key = B.key; and SELECT ... FROM A LEFT OUTER JOIN B ON .... RIGHT OUTER and FULL OUTER join forms also exist (though some engines omit FULL OUTER). Therefore, the assertion is correct in standard SQL and in the majority of modern SQL products. Step-by-Step Solution: Identify the standard syntax: JOIN ... ON.Enumerate join types: INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER.Confirm that ON specifies the join predicate.Conclude the claim is correct. Verification / Alternative check: Write examples and observe equivalent results to legacy WHERE-based inner joins, while outer joins require the explicit OUTER JOIN forms. Why Other Options Are Wrong: “Incorrect” and “proprietary only” ignore the standard. “WHERE for outer joins” is false; WHERE cannot generate unmatched rows from the outer table. “Indeterminate without ANSI mode” is misleading; most engines implement this syntax regardless of toggles. Common Pitfalls: Using WHERE filters that accidentally turn an outer join into an inner join; misplacing join predicates versus filtering predicates. Final Answer: Correct

Question 19

Stored procedures — evaluate the benefit statement: “One benefit of using a stored procedure is that it can be distributed to client computers.” Determine whether this is correct or incorrect, considering where stored procedures execute.

Accepted Answer

Introduction / Context: Stored procedures encapsulate SQL logic on the database server. They improve performance, security, and maintainability by centralizing code and reducing chattiness between client and server. The prompt suggests that a benefit is the ability to distribute procedures to clients, which tests understanding of where stored procedures live and execute. Given Data / Assumptions: Stored procedures are database objects compiled and stored on the server. Clients invoke procedures with CALL/EXEC or equivalent. Client applications might have local code, but server-side procedures remain server-resident. Concept / Approach: The principal advantages of stored procedures include centralized logic, reduced network round-trips (one call versus many statements), shared security context, plan reuse, and version control on the server. Distribution to clients is not a benefit; in fact, one reason to use procedures is to avoid scattering SQL across clients. Client distribution applies to application binaries or libraries, not to stored procedures themselves. Step-by-Step Solution: Identify where procedures are stored: on the server catalog.Identify how they are executed: by the server process, not client runtime.Assess the claimed benefit: contradicts centralization and server execution.Conclude the statement is incorrect. Verification / Alternative check: Deploy a procedure and update it centrally; all clients pick up changes immediately without redistribution—evidence that procedures are not distributed to clients for execution. Why Other Options Are Wrong: Architecture tiering does not relocate procedures to clients. Client-side plan caches do not equate to distributing procedures. Network characteristics do not change where procedures reside. Common Pitfalls: Confusing ORMs or embedded SQL libraries with stored procedures; assuming code distribution models apply to server-resident database logic. Final Answer: Incorrect

SQL for Database Construction Questions