Kafka Consumer Groups and Rebalancing Explained
How Kafka consumer groups share partitions, what triggers a rebalance, and how cooperative assignment and static membership cut downtime.
~8 min readread →
Developer interview notes & guides — Page 2
Focused explanations and longer guides, with worked scenarios, common mistakes and interview exercises across every chapter.
level:
Showing 25–48 of 825 articles
Kafka Exactly-Once Semantics and Transactions Explained
How Kafka transactions make consume-transform-produce exactly-once, how zombie fencing and read_committed work, and where the guarantee ends.
~8 min readread →
Kafka Idempotent Producer, Batching and linger.ms
How the Kafka idempotent producer stops duplicates from retries, what it cannot dedupe, and how batch.size, linger.ms and compression work.
~8 min readread →
Kafka Offset Commits and Delivery Semantics Explained
How Kafka consumers commit offsets, why commit order decides at-most-once or at-least-once, and how to get exactly-once effects.
~8 min readread →
Kafka Partitions, Offsets and Message Ordering Explained
How Kafka partitions and offsets work, why ordering is only per partition, and how keys keep each entity's events in sequence.
~9 min readread →
Kafka Poison Messages, Retries and Dead Letter Topics
Why one bad Kafka record can block a whole partition, and how to handle it with error classification, bounded retries and a dead letter topic.
~9 min readread →
Kafka Replication, ISR and acks=all: Durability Explained
How Kafka replicates partitions, what the ISR and high watermark mean, and how acks=all with min.insync.replicas prevents data loss.
~8 min readread →
Kafka Retention vs Log Compaction: How Old Data Is Removed
How Kafka deletes old segments by time or size, how log compaction keeps the latest value per key, and how tombstones delete keys.
~8 min readread →
Embeddings and Vector Search: AI Engineer Interview Guide
What embeddings are, how cosine similarity and approximate nearest neighbour indexes work, and when to add keyword search and reranking.
~8 min readread →
How to Evaluate LLM Applications: Evals and LLM Judges
Build an eval suite for an LLM app: golden datasets, code-based checks, LLM-as-judge calibration, retrieval metrics and regression gates in CI.
~8 min readread →
Fine-Tuning vs RAG: When to Use Each in LLM Apps
Fine-tuning changes how a model behaves; RAG changes what it knows at request time. A decision guide with data prep, costs and failure cases.
~9 min readread →
LLM Function Calling and Agent Loops Explained
How LLM tool use works: schemas, the call-execute-return loop, validation, error results, step limits and when an agent beats a workflow.
~8 min readread →
Prompt Injection Attacks and Defenses for LLM Apps
Direct and indirect prompt injection, why prompts alone cannot stop it, and layered defenses: tool policy, confirmation and output filtering.
~9 min readread →
How to Design a RAG Pipeline: Chunking, Retrieval and Prompts
A step-by-step RAG design for interviews: ingestion, structure-aware chunking, hybrid retrieval, grounded prompts with citations and evaluation.
~9 min readread →
How to Reduce LLM Hallucinations in Production
Why LLMs hallucinate and the layered fixes that work: grounding, permission to abstain, citations, structured output and automatic claim checks.
~8 min readread →
LLM Tokens, Temperature and Top-p Sampling Explained
How LLMs turn text into tokens, why the context window is a shared budget, and what temperature, top-k and top-p actually do to the next token.
~8 min readread →
Bias-Variance Tradeoff Explained: ML Interview Guide
What bias and variance really measure, how to read validation and learning curves, and which fix to reach for when a model underfits or overfits.
~9 min readread →
Cross-Validation and Data Leakage in Machine Learning
How k-fold cross-validation works, which splitter suits grouped or time data, and how leakage turns pure noise into an 85% accurate model.
~8 min readread →
Gradient Descent Explained: Batch vs SGD vs Mini-Batch
How gradient descent updates weights, how batch, stochastic and mini-batch differ, and why learning rate and feature scaling decide convergence.
~9 min readread →
Imbalanced Classification: Class Weights, SMOTE and PR-AUC
How to train and evaluate a classifier with rare positives: PR-AUC over accuracy, class weights vs thresholds, and resampling without leakage.
~8 min readread →
Logistic Regression Interview Questions: Sigmoid, Log Loss, Odds
How logistic regression turns a linear score into a probability, why it uses log loss, how to read odds ratios and what separation breaks.
~8 min readread →
Overfitting and L1 vs L2 Regularization Explained with Code
How to spot overfitting, why L1 zeroes weights while L2 only shrinks them, and how scaling, early stopping and dropout fit into regularization.
~9 min readread →
Precision vs Recall and F1 Score: ML Interview Guide
Precision, recall and F1 from the confusion matrix, how the threshold trades one for the other, and how to pick one that meets a target.
~8 min readread →
Random Forest vs Gradient Boosting: Bagging vs Boosting
Why a random forest averages away variance while gradient boosting chips away bias, with runnable code, early stopping and when to choose each.
~8 min readread →
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