I went straight to the Enrollment table and started listing columns before thinking about the constraint properly.
Start by identifying the core entities and their relationships, then normalize to 3NF to eliminate redundancy. Define primary keys, foreign keys, and constraints to enforce business rules, and finally add indexes to optimize common query patterns. Explain trade-offs between normalization and performance, especially for OLTP workloads.
Pro tip: Demonstrate awareness of real-world constraints: mention that while strict normalization is ideal, denormalization might be considered for read-heavy analytics, but for OLTP, prioritize data integrity and write efficiency. Also, discuss how to handle the 'same course, same term' constraint using a composite unique key or a check constraint with a subquery.
List all entities (Students, Courses, Departments, Instructors, Enrollments) and define their relationships (e.g., a student enrolls in multiple courses, a course belongs to a department, an instructor teaches a course).
Create tables for each entity with appropriate attributes, ensuring 3NF. Use surrogate or natural primary keys, and add foreign keys to enforce referential integrity.
Implement constraints such as unique composite key on (student_id, course_id, term) to prevent duplicate enrollments in same course/term, and a check constraint or lookup table for grade values.
Identify frequent query patterns (e.g., enrollments by student, courses by department) and create indexes on foreign keys and columns used in WHERE, JOIN, and ORDER BY clauses.
Explain how normalization ensures data integrity but may require joins; indexes speed reads but slow writes. Mention partitioning or other techniques for large-scale OLTP.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
SCD Type 2 is one of those things I know conceptually but always stumble explaining live.
Start by clarifying the business questions and analytics requirements to define the grain of FactEnrollment. Then design the star schema with appropriate dimensions, surrogate keys, and SCD Type 2 for the student dimension, explaining how point-in-time correctness is achieved. Finally, discuss implementation considerations and trade-offs.
Pro tip: Emphasize that the grain should be the most atomic level possible to allow flexible aggregation, and that SCD Type 2 requires careful handling of surrogate keys and effective dating to ensure accurate historical reporting.
Ask questions to understand what enrollment metrics are needed, such as counts, trends, and student demographics. Identify the key performance indicators (KPIs) and the level of detail required.
Determine the most atomic level of data, such as one row per student per course per term. This ensures flexibility for aggregation and avoids double-counting.
List the additive measures (e.g., enrollment count, credits) and the dimension tables (e.g., Student, Course, Term, Date). Design surrogate keys for each dimension.
Explain how to track changes in student attributes like major by adding new rows with effective dates and current flags. Use surrogate keys to link fact rows to the correct dimension version.
Describe how to join facts to dimensions using the surrogate keys and date ranges to reflect the state at the time of enrollment. Discuss ETL processes to maintain SCD Type 2.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the query patterns and data characteristics, then propose a partitioning strategy (e.g., range partitioning on enrollment date) and a clustering strategy (e.g., cluster by student_id) that align with those patterns. Justify choices by discussing how they improve performance for common queries and simplify maintenance tasks like data loading and purging.
Pro tip: Mention that partitioning and clustering should be driven by the most frequent and expensive queries, and that you would validate the design with real query plans and performance metrics before full implementation.
Identify the most common queries (e.g., by date range, student, course) and data volume growth. Determine if queries are point lookups, range scans, or aggregations.
Select a partitioning key that aligns with query filters and maintenance needs. For example, partition by enrollment_date (monthly or yearly) to enable partition pruning and easy archival.
Within each partition, cluster by a column frequently used in filters or joins, such as student_id, to co-locate related rows and speed up point queries.
Explain how the choices improve query performance (e.g., partition pruning, clustered index seeks) and maintenance (e.g., partition switching for data loads, dropping old partitions).
Discuss how the design handles data growth, index maintenance, and potential skew. Mention monitoring and possible adjustments over time.
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Self-join or two CTEs, I went with two CTEs and an inner join on student_id and term_id.
Clarify the schema and then use a self-join or GROUP BY with HAVING to find students enrolled in both courses in the same term. Ensure the query handles duplicates and returns distinct students.
Pro tip: Mention that you would verify the grain of the enrollment table and consider indexing on (student_id, term, course_id) for performance, as Amazon values scalability.
Ask about table structure (e.g., enrollments with student_id, course_id, term) and confirm that 'same term' means identical term value. State any assumptions.
Decide between a self-join on student_id and term with course filters, or GROUP BY student_id, term with HAVING COUNT(DISTINCT course_id) = 2 and course IN ('CS101','MATH201').
Construct the query, ensuring it returns distinct students. For self-join, use aliases and filter each side for the respective course.
Consider duplicates, NULLs, and indexing. Discuss how the query scales with large data.
Walk through a sample dataset to verify correctness and explain the logic clearly.
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This one took me longer than I'd like to admit.
First, identify the most recently completed term using a subquery or CTE. Then compute the credit-weighted average GPA per department for that term, and rank departments using a deterministic tie-breaker such as department name. Finally, return the top 3 departments.
Pro tip: Explicitly state your assumptions about the data model (e.g., how terms are identified, how credits are stored) and mention that you would validate the query against edge cases like ties or missing data.
Use a subquery or CTE to find the maximum term identifier (e.g., MAX(term_id) or MAX(term_date)) from the enrollments or terms table.
Join enrollments, courses, and departments, filter for the most recent term, and calculate SUM(grade_points * credits) / SUM(credits) grouped by department.
Use a window function like RANK() or ROW_NUMBER() with ORDER BY weighted_gpa DESC, department_name ASC to ensure ties are broken consistently.
Filter the ranked results to keep only rows where rank <= 3, and return the department name and weighted GPA.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Gave the standard normalization vs denormalization angle.
Start by contrasting OLTP and OLAP design goals—transactional integrity and speed vs. analytical query performance and historical analysis—using the university scenario to illustrate. Then, explain the star vs. snowflake schema trade-off, focusing on normalization, query complexity, and performance, and conclude with when to choose snowflake over star based on specific requirements.
Pro tip: Tie your answer to Amazon's leadership principles: emphasize customer obsession by choosing the schema that best serves the end-user's query patterns, and insist on the highest standards by acknowledging that the 'right' choice depends on data volume, query complexity, and maintenance trade-offs.
Describe OLTP as handling day-to-day operations like student registration and grade entry, requiring high concurrency and ACID compliance. Describe OLAP as supporting analytical queries like enrollment trends and performance dashboards, optimized for read-heavy, complex aggregations.
Contrast normalization (OLTP) vs. denormalization (OLAP), row-store vs. column-store, and indexing strategies. Highlight how OLTP prioritizes write efficiency and data integrity, while OLAP prioritizes read speed and analytical flexibility.
Define star schema as a denormalized structure with a central fact table and dimension tables, and snowflake schema as a normalized version where dimensions are split into multiple tables. Mention that star is simpler and faster for queries, while snowflake reduces redundancy and storage.
Choose snowflake when dimension tables are large and highly normalized, storage is a concern, or when complex hierarchies require frequent updates. Also consider it when the ETL process can handle the added complexity and query performance is acceptable.
Recommend a hybrid or context-specific approach: use OLTP for operational systems and OLAP with a star schema for most analytics, but opt for snowflake if the university's data has complex dimensions (e.g., multi-level academic hierarchies) and storage efficiency is critical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.