AI / ML Tests

Prompt Engineering — Advanced

Advanced prompting: eval harnesses, adversarial robustness, multi-step agents, cost/latency tradeoffs, grounding quality, and production governance. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Prompt Engineering — Intermediate

Intermediate prompting: chain-of-thought, structured outputs, evaluation, RAG vs prompting, context limits, and injection awareness. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

Prompt Engineering — Beginner

Foundations of prompting LLMs: clarity, roles, few-shot examples, output format, and basic safety. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.

LLM Applications — Advanced

RAG failure modes, agents/tools, LLM-as-judge caveats, jailbreaks, RAG vs fine-tune, KV cache, semantic cache, ACL retrieval, speculative decoding, and SLOs.

LLM Applications — Intermediate

RAG chunking/retrieval, prompt injection, tools, structured outputs, evals, hybrid search, cost/latency, memory, guardrails, and faithfulness.

LLM Applications — Beginner

LLM basics: tokens, temperature, context window, hallucinations, system prompts, RAG/embeddings intro, few-shot, rate limits, and safety.

ML Fundamentals — Advanced

Bias–variance, calibration, interpretability caveats, imbalance metrics, deep learning training dynamics, transfer/domain shift, causality limits, nested CV, and drift.

ML Fundamentals — Intermediate

Regularization, ROC/F1, imbalance, scaling, encodings, ensembles, leakage, early stopping, losses, softmax, embeddings, and tuning discipline.

ML Fundamentals — Beginner

Supervised vs unsupervised, splits, overfitting, labels, classification vs regression, metrics basics, loss, gradient descent, and cross-validation.