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Prompt Engineering & Applied Generative AI Practice Tests
Prompt Engineering & Applied Generative AI Practice Tests
Master Chain-of-Thought, RAG architectures, LLM guardrails, vector search, agentic workflows, and token optimization.
Created bySantosh Harge
Last updated 11/2026
English
What you'll learn
- Master advanced prompt techniques like Few-Shot, Chain-of-Thought, Directional-Stimulus, and ReAct for enterprise LLMs.
- Architect Retrieval-Augmented Generation (RAG) systems, vector embeddings, semantic search, and document chunking strategies.
- Mitigate LLM hallucinations, implement safety guardrails, manage context windows, and optimize inference token costs.
- Evaluate LLM performance, perform fine-tuning trade-offs, and orchestrate AI agents using modern framework patterns.
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Included in This Course
260 questions
Description
Are you ready to move beyond basic prompts and build reliable, high-performance Generative AI applications?
This comprehensive practice test suite is engineered to evaluate your practical capabilities in prompt engineering, Retrieval-Augmented Generation (RAG), vector database integration, and LLM application architecture. Designed for AI developers, software engineers, product leads, and cloud architects, these scenario-based mock tests challenge your technical depth across modern Generative AI deployment patterns.
What you will get in this course:
• Full-length practice exams covering real-world prompt engineering techniques, agentic frameworks, and LLM evaluation strategies.
• Detailed explanations for every question, breaking down prompt structures, parameter choices (temperature, top_p), and distractor options.
• In-depth evaluation of advanced prompt patterns: Zero-Shot, Few-Shot, Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), Directional-Stimulus, and ReAct prompting.
• Scenario-based testing of RAG architectures (embedding models, chunking strategies, hybrid search), vector databases, safety guardrails, PII redaction, and hallucination mitigation.
• Comprehensive scenarios on API parameter tuning, context window management, cost optimization, and multi-agent system orchestration.
Why take these practice tests?
Deploying enterprise GenAI applications requires balancing accuracy, latency, security, and token costs. Practicing under timed test conditions sharpens your decision-making and ensures complete job-readiness for modern Applied AI and Prompt Engineering roles.
Enroll today to test your skills, bridge your operational knowledge gaps, and master Applied Generative AI!
Who this course is for:
- Software engineers, AI developers, product managers, data professionals, and solution architects looking to master prompt engineering and build production-grade Generative AI applications.