12 worked SQL problems showing exactly when to use CTEs vs subqueries. Covers readability, performance, correlated subqueries, recursive CTEs for hierarchies and date series, and multi-CTE pipeline queries with full solutions.
Implement ML algorithms from scratch in Python and answer the conceptual questions that trip up candidates. Covers logistic regression, K-means, ROC-AUC, cross-validation, feature engineering, gradient boosting, and LLM questions for 2026 interviews.
Practice 15 SQL interview questions for search and advertising platform roles. Covers CTR analysis, sessionization, attribution models, ranking queries, anomaly detection, and multi-table pipeline queries with full solutions.
Prepare for statistics and A/B testing interview questions with worked Python examples. Covers Bayes theorem, p-values, sample size calculation, power analysis, chi-squared tests, bootstrap confidence intervals, and common scenario questions.
Practice the 14 Pandas patterns that appear most often in data science technical screens. Covers loc vs iloc, groupby, merge, apply vs vectorization, null handling, pivot tables, and time series with full Python solutions.
The hardest SQL interview category explained. 13 worked problems covering DAU/MAU calculations, cohort tables, N-day retention, churn detection, date spine technique, and power user curves with full SQL solutions.
Practice 15 SQL interview questions tailored to e-commerce and marketplace data science roles. Covers customer analytics, product ranking, funnel queries, seller metrics, and time-based business intelligence with full solutions.
Master every SQL window function pattern tested in data science interviews. 14 worked problems covering ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals, NTILE, and complex multi-window queries with full solutions.
LLM and AI Engineer interviews in 2026 demand deep practical knowledge of production systems, moving beyond basic textbook theory like gradient descent or BERT mechanics. Successful candidates must demonstrate mastery over modern architectural challenges, including the shift from PPO to DPO in alignment pipelines and debugging complex RAG retrieval failures. This guide outlines a structured preparation path covering transformer fundamentals, production RAG system design, and agentic architectures using ReAct and MCP standards. Junior candidates focus on attention mechanisms and tokenization, while senior roles require reasoning through multi-agent system design and cost optimization at scale. Key study topics include implementing scaled dot-product attention, understanding Chinchilla scaling laws, and deploying QLoRA fine-tuning on custom datasets. By mastering these 50 curated questions sourced from Google, Meta, and Anthropic loops, engineers can confidently navigate technical screens involving system design, safety alignment frameworks like Constitutional AI, and high-scale inference optimization using vLLM and PagedAttention.
