HikeLetter | PMP Training & Certification Online Course | Project Management Learning

Overview

Course Description

Duration: 60 minutes
Question count: 50–60 multiple-choice questions
Exam fee: US$125 listed by NVIDIA; regional pricing may vary.
Passing score: Not stated on the official exam page.
Validity: Two years.

Course Questions

10 Questions

A team is building an enterprise LLM assistant and asks: What is the primary purpose of a large language model (LLM)?

A. Generate or transform language based on learned patterns B. Store relational tables only C. Replace network protocols D. Guarantee factual accuracy

A team is building an enterprise LLM assistant and asks: What is tokenization?

A. Splitting text into model-consumable units B. Encrypting model weights C. Converting every word into an image D. Deleting database records

A team is building an enterprise LLM assistant and asks: What is the role of embeddings in RAG?

A. Represent content as vectors for semantic search B. Encrypt the whole database C. Guarantee no hallucinations D. Replace all prompts

A team is building an enterprise LLM assistant and asks: What best describes retrieval-augmented generation (RAG)?

A. Retrieve relevant information and supply it as model context B. Train a new model for every query C. Use only fixed rules D. Compress each document into one token

A team is building an enterprise LLM assistant and asks: Why chunk documents before indexing them for RAG?

A. Create manageable passages for retrieval B. Make all documents identical C. Remove the need for embeddings D. Prevent every retrieval error

A team is building an enterprise LLM assistant and asks: What is prompt engineering?

A. Designing instructions and context to guide model behavior B. Upgrading a GPU physically C. Writing only database indexes D. Removing model evaluation

A team is building an enterprise LLM assistant and asks: What is a hallucination in generative AI?

A. Unsupported or incorrect information presented as true B. A successful backup C. GPU memory allocation D. A verified citation

A team is building an enterprise LLM assistant and asks: Which approach can help reduce unsupported answers?

A. Retrieve trusted sources and ground answers in that evidence B. Increase randomness without testing C. Remove source documents D. Ask the model to sound confident

A team is building an enterprise LLM assistant and asks: What is fine-tuning?

A. Further training a pretrained model on targeted examples B. Changing prompt font C. Searching a vector database D. Adding a firewall

A team is building an enterprise LLM assistant and asks: What is a benefit of parameter-efficient fine-tuning such as LoRA?

A. Fewer parameters need to be trained B. Every model parameter is always retrained C. Data quality becomes irrelevant D. Model drift is impossible

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