Python AI for Langchain/Llamaindex Test

Evaluates knowledge of LangChain’s architecture, including the design and orchestration of chains, prompt templates, memory components, and modular LLM pipelines. Focus areas include LLMChain, SequentialChain, RetrievalQA, prompt templating strategies, conversation history management, callbacks, and chain tracing. The section emphasizes best practices for creating maintainable, composable AI workflows, and leveraging prebuilt utilities for common tasks like summarization, translation, and document retrieval.

Measures the candidate’s ability to create, manage, and query data indices efficiently using LlamaIndex. Topics include document ingestion, text chunking strategies, metadata management, vectorization, and the design of query engines for both structured and unstructured data. Also covers the use of VectorStoreIndex, ListIndex, and TreeIndex, multi-index querying, query routing, and composable graph architectures. Emphasis is placed on optimizing retrieval performance and contextual relevance in large data environments.

Assesses mastery in designing and implementing RAG systems that connect language models to external data sources. Covers the end-to-end architecture of RAG pipelines, including embedding generation, document retrieval, context fusion, reranking, grounding accuracy, and response synthesis. Tests the ability to handle multi-hop reasoning, hybrid retrieval (structured + unstructured), and caching for improved efficiency. Also includes design patterns for context window management, data freshness handling, and latency optimization in real-world AI solutions.

Evaluates understanding of how vector databases and embedding models form the backbone of retrieval-augmented systems. Includes topics like vector representation, similarity search (cosine, dot product, Euclidean), and storage optimization. Tests practical knowledge of ChromaDB, FAISS, and Pinecone, embedding model selection (OpenAI, HuggingFace, Cohere), and index tuning techniques. Also includes embedding evaluation, clustering strategies, and vector dimensionality considerations for balancing recall, precision, and latency.

Focuses on the creation and orchestration of autonomous, reasoning-driven agents using LangChain and LangGraph. Candidates are assessed on their ability to implement AgentExecutor, define and register custom tools, manage planner–executor patterns, and build decision graphs for multi-step workflows. The topic also tests understanding of collaborative multi-agent ecosystems, context passing between agents, error handling in agent loops, and performance optimization using graph visualization for debugging and traceability.

Measures proficiency in integrating LangChain and LlamaIndex with major cloud LLM ecosystems such as Azure OpenAI, AWS Bedrock, and Google Vertex AI. Includes secure API authentication, endpoint configuration, token management, and hybrid model orchestration (cloud + local). Evaluates knowledge of latency management, scalability, cost optimization, and cross-provider model chaining. Also covers troubleshooting connectivity, managing usage quotas, and enabling cloud-based monitoring for distributed AI applications.

Assesses understanding of responsible and ethical AI deployment practices, focusing on moderation frameworks and evaluation methodologies. Topics include input/output moderation using OpenAI’s moderation API and Guardrails AI, prompt toxicity filtering, bias mitigation, and model auditing. Evaluates the candidate’s ability to implement evaluation frameworks such as LangSmith, TruLens, and PromptLayer for continuous model performance analysis (faithfulness, coherence, relevance). Also covers compliance standards, transparency logging, and governance for Responsible AI.

Tests knowledge of deploying, scaling, and monitoring LangChain/LlamaIndex applications in production environments. Topics include containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, and async task scaling using Celery or Ray. Also covers performance monitoring (Prometheus, Grafana), distributed tracing (LangSmith), and failover handling. Focus is placed on optimizing response latency, parallel chain execution, and automated retraining pipelines. Candidates must demonstrate understanding of DevOps practices for resilient AI deployment.

The capstone topic assessing enterprise-grade AI architecture design and real-world implementation capability. Encompasses composable graph-based architectures, distributed RAG systems, multi-agent orchestration frameworks, and pipeline observability using LangGraph. Tests deep knowledge of hybrid retrieval (cross-modal data), edge inference (NVIDIA Jetson, ONNX Runtime), and data privacy-aware design. Includes topics on explainability (trace graphs, prompt lineage), security (encryption, API tokens), version control for chains, and lifecycle management for continuous AI improvement.