Preparing for a database development interview requires a solid understanding of both theoretical concepts and practical implementation. Employers often test candidates on a range of topics, from foundational database principles to advanced optimization techniques. Below is a comprehensive guide to the most common questions you might encounter, along with explanations to help you craft strong answers.
1. Foundational Database Concepts
Q: What are the differences between relational and non-relational databases?
This question evaluates your grasp of database types. Relational databases (e.g., MySQL, PostgreSQL) use structured schemas with tables, rows, and columns, enforcing ACID properties. Non-relational databases (e.g., MongoDB, Cassandra) prioritize flexibility, scalability, and unstructured data storage, often sacrificing strict consistency for performance.
Q: Explain ACID properties in database transactions.
ACID stands for Atomicity, Consistency, Isolation, and Durability. Interviewers want to ensure you understand how databases maintain reliability. For example, atomicity ensures transactions are "all or nothing," while isolation prevents concurrent transactions from interfering.
2. SQL and Query Optimization
Q: Write a SQL query to find the second-highest salary from an "Employees" table.
This tests your SQL syntax and problem-solving skills. A common approach uses subqueries or window functions:
SELECT MAX(salary) FROM Employees WHERE salary < (SELECT MAX(salary) FROM Employees);
Q: How do you optimize a slow-running SQL query?
Discuss indexing, query refactoring, and execution plan analysis. Mention tools like EXPLAIN in PostgreSQL to identify bottlenecks. For example, adding indexes on frequently filtered columns or avoiding SELECT * can improve performance.
3. Database Design and Normalization
Q: What is database normalization, and why is it important?
Normalization reduces data redundancy by organizing tables into smaller, related entities. Explain the first three normal forms (1NF, 2NF, 3NF) and their goals. For instance, 2NF eliminates partial dependencies, while 3NF removes transitive dependencies.
Q: When would you denormalize a database?
Denormalization improves read performance for analytical workloads (e.g., data warehouses). Trade-offs include increased storage and potential data inconsistency.
4. Transactions and Concurrency Control
Q: What is a deadlock, and how can it be resolved?
A deadlock occurs when two transactions block each other. Solutions include setting timeout policies, using deadlock detection algorithms, or redesigning transaction logic to access resources in a fixed order.
Q: Explain the differences between READ COMMITTED and SERIALIZABLE isolation levels.
READ COMMITTED prevents dirty reads but allows non-repeatable reads. SERIALIZABLE ensures strict isolation by locking entire ranges, preventing phantom reads but sacrificing performance.
5. Database Security and Backup
Q: How would you prevent SQL injection attacks?
Use parameterized queries or ORM frameworks to sanitize inputs. Avoid dynamic SQL generation with user-provided strings.
Q: Describe a backup strategy for a high-availability database.
Discuss full backups, incremental backups, and replication (e.g., master-slave setups). Mention tools like AWS RDS snapshots or PostgreSQL’s WAL archiving.
6. Real-World Scenarios
Q: Design a database schema for an e-commerce platform.
Expect to outline tables for users, products, orders, and payments. Highlight relationships (e.g., one-to-many between users and orders) and indexing strategies for search-heavy fields like product names.
Q: How would you migrate data from a legacy system to a new database?
Emphasize steps like schema mapping, data validation, and phased rollouts. Tools like AWS DMS or custom ETL scripts are often used.
7. Advanced Topics
Q: What is sharding, and how does it improve scalability?
Sharding splits a database into smaller, manageable pieces (shards) distributed across servers. It reduces load on individual nodes but complicates cross-shard queries.
Q: Explain CAP theorem in the context of distributed databases.
CAP states that a distributed system can only guarantee two of three properties: Consistency, Availability, and Partition Tolerance. For example, MongoDB prioritizes AP, while PostgreSQL focuses on CP.
Final Tips for Success
- Practice writing complex SQL queries and analyzing execution plans.
- Review real-world case studies on scalability and disaster recovery.
- Demonstrate problem-solving skills by discussing trade-offs (e.g., consistency vs. performance).
By mastering these areas, you’ll be well-prepared to tackle technical questions and showcase your expertise in database development.
