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Start by scanning the code for common vulnerability patterns and correctness pitfalls, then systematically evaluate each area: security, correctness, data modeling, concurrency, and reliability. For each issue, clearly state the problem, its impact, and a concrete fix, prioritizing by severity. Conclude by summarizing the most critical issues and suggesting preventive measures.
Pro tip: Demonstrate a security-first mindset by highlighting injection and authentication flaws early, and tie fixes to established best practices (e.g., OWASP, ACID, idempotency) to show depth beyond surface-level bugs.
Identify vulnerabilities such as SQL injection, insecure deserialization, hardcoded secrets, and missing input validation. Propose fixes like parameterized queries, environment variables, and input sanitization.
Check for off-by-one errors, incorrect conditionals, resource leaks, and improper error handling. Suggest fixes like boundary checks, context managers, and specific exception handling.
Examine schema design, normalization, constraints, and transaction boundaries. Recommend fixes such as adding foreign keys, unique constraints, and using transactions for atomicity.
Look for race conditions, deadlocks, lack of idempotency, and single points of failure. Propose fixes like locks, retries with backoff, and idempotent operations.
Rank issues by severity and impact, then summarize the top fixes and suggest preventive measures like code reviews and automated testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by systematically scanning the codebase for patterns that indicate hardcoded secrets, such as API keys, passwords, or tokens. Then, explain a remediation plan that includes removing the secrets, rotating them, and implementing secure secret management practices. Emphasize prevention through automated scanning and secure coding guidelines.
Pro tip: Mention that you would also check version control history (e.g., git log) because secrets may have been committed and later removed, but still exist in history. This shows thoroughness and awareness of real-world security incidents.
Use static analysis tools (e.g., TruffleHog, GitLeaks, or custom regex) to scan the codebase for patterns like 'password=', 'api_key=', 'secret=', and high-entropy strings. Manually review configuration files and source code for suspicious literals.
Determine which secrets are active and what access they grant. Immediately rotate any exposed credentials to invalidate them, and assess potential unauthorized access or data breaches.
Replace hardcoded secrets with references to environment variables or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Salesforce Shield). Ensure secrets are not stored in plaintext in the repository.
Integrate a secrets management solution that provides encryption, access control, and audit logging. Use environment-specific configurations and inject secrets at runtime.
Add pre-commit hooks and CI/CD pipeline checks to scan for secrets. Educate the team on secure coding practices and establish a process for secret rotation and incident response.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the context and requirements of the schema or query, then systematically review for common issues like normalization, indexing, and query performance. Prioritize improvements based on impact and trade-offs, and suggest concrete changes with justifications.
Pro tip: Always consider the trade-offs between normalization and denormalization, and how they affect read/write performance and scalability—Salesforce values engineers who can balance theoretical purity with practical constraints.
Ask about the application's read/write patterns, data volume, and performance goals to understand what 'good' looks like for this schema.
Check for normalization problems, missing constraints, inappropriate data types, and lack of indexing on frequently queried columns.
Look for inefficient queries such as SELECT *, missing WHERE clauses, N+1 queries, and improper JOINs that could be optimized.
Suggest specific changes like adding indexes, refactoring queries, or denormalizing for performance, and explain the trade-offs involved.
Rank the improvements by impact and effort, and summarize the key recommendations to demonstrate structured thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the specific database operations and isolation level in use, then systematically identify race conditions such as lost updates, dirty reads, or write skew. Propose concrete fixes like optimistic locking, pessimistic locking, or transaction isolation adjustments, and explain the trade-offs between performance and consistency.
Pro tip: Demonstrate maturity by acknowledging that not all race conditions need fixing—sometimes business requirements tolerate eventual consistency. Always tie your solution to the actual consistency requirements and performance impact, showing you understand trade-offs.
Ask about the specific operations, expected concurrency, and consistency requirements to ground your analysis in a real context.
Walk through potential anomalies like lost updates, non-repeatable reads, or phantom reads, explaining how they could occur in the given operations.
Suggest appropriate fixes such as optimistic concurrency control (versioning), pessimistic locking, or adjusting transaction isolation levels.
Discuss the impact of each fix on performance, scalability, and complexity, and recommend the best option based on the scenario.
Recap your solution, confirm it meets the consistency needs, and mention how you would test it under concurrent load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.