Everything in AI & ML, page 6
Evaluating image generation: FID and IS
FID compares feature distributions of real and generated images, lower is better; Inception Score rewards confident, diverse classes but ignores real data.
Designing an agent that resolves ambiguity
Detect ambiguity, gather evidence with the contact API, resolve relative time deterministically, ask the user only when genuinely uncertain, then confirm before the irreversible booking.
Securing tool-using LLM agents
Name indirect prompt injection, data exfiltration, and unsafe tool execution, then defend with sandboxing, least-privilege scoped tools, input/output filtering, and human-in-the-loop on risky actions.
Hybrid search and re-ranking for retrieval
Hybrid search fuses dense semantic and sparse keyword signals to catch exact terms dense misses; a cross-encoder re-ranker rescoring top-k boosts precision.
Evaluating a RAG system end to end
Measure retrieval with context recall or precision, and generation with faithfulness and answer relevance, attributing failures to the right stage.
Direct Preference Optimization explained
DPO reparameterizes the RLHF reward in terms of the policy itself, turning alignment into a simple classification loss on preference pairs with no separate reward model or PPO.
Reward models in RLHF and PPO
It learns from human preference comparisons to score responses, then supplies the reward signal that PPO maximizes while a KL penalty keeps the policy near the reference.
Pre-training versus fine-tuning an LLM
Pre-training is broad self-supervised next-token prediction on huge corpora at massive cost; fine-tuning adapts on small labeled data cheaply.
Demographic Parity versus Equalized Odds in hiring
Demographic parity equalizes selection rates regardless of qualification; equalized odds equalizes true and false positive rates across groups, conditioning on the true label.
Explain an interaction effect to a non-statistician
Define interaction as it depends on, show separate slope lines per age group, give the business takeaway on targeting.
Present a small but significant A/B test lift
Hypothesis, design and validity checks, result with effect size and interval, business impact of 0.5%, then a clear recommendation.
Interactive versus static plots for EDA
Interactive libraries win for exploring dense, high-cardinality, or multi-dimensional data via zoom, hover, and filtering; static plots win for reproducible, publication output.
Parquet versus CSV for analytical data lakes
Parquet stores by column enabling projection pushdown, compression, and predicate skipping; CSV is row-based, untyped, and slow to scan.
Audit an ML pipeline for GDPR compliance
Inventory data and check minimization, verify processing matches stated purpose, build lineage to trace any prediction's inputs.
A/B test two fraud models in production
Randomize by entity, consider shadow mode first, collect precision/recall and business loss, decide with significance and guardrails.
Communicate a forecast interval to an executive
Give the point estimate but frame the range as scenarios, use a fan chart, tie the interval to planning decisions and risk.
Purpose of watermarks in Spark Structured Streaming
A watermark sets a threshold on event-time lateness, lets late data update windows up to that bound, and tells Spark when to finalize and drop old state.
repartition() versus coalesce() in Spark
Repartition does a full shuffle and can increase or balance partitions; coalesce avoids a full shuffle and only reduces them.
Explain a loan denial with LIME or SHAP
LIME fits a local surrogate, SHAP attributes the prediction across features via Shapley values, both give per-feature contributions.
Design an automated A/B test reporting system
Standardized metric definitions, automated stats with confidence intervals and guardrails, segment breakdowns, a clear ship recommendation.
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