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New NCA-GENL Cram Materials | NCA-GENL Certification Sample Questions

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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.

Topic 2

Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

Topic 3

Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

Topic 4

Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:

Topic 5

LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.

Topic 6

Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.

Topic 7

Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.

Topic 8

Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

NVIDIA Generative AI LLMs Sample Questions (Q70-Q75):

NEW QUESTION # 70
When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

A. Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.
B. Subword tokenization reduces the model's computational complexity by eliminating embeddings.
C. Subword tokenization removes punctuation and special characters to simplify text input.
D. Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.

Answer: D

Explanation:
Subword tokenization, such as Byte-Pair Encoding (BPE) or WordPiece, is critical for preprocessing text data in LLM fine-tuning because it breaks words into smaller units (subwords), enabling the model to handle rare or out-of-vocabulary (OOV) words effectively. NVIDIA's NeMo documentation on tokenization explains that subword tokenization creates a vocabulary of frequent subword units, allowing the model to represent unseen words by combining known subwords (e.g., "unseen" as "un" + "##seen"). This improves generalization compared to word-based tokenization, which struggles with OOV words. Option A is incorrect, as tokenization does not eliminate embeddings. Option B is false, as vocabulary size is not fixed but optimized.
Option D is wrong, as punctuation handling is a separate preprocessing step.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 71
You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?

A. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.
B. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.
C. RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.

Answer: A

Explanation:
RAPIDS is a suite of open-source libraries designed to accelerate data science workflows by leveraging GPU processing, as emphasized in NVIDIA's Generative AI and LLMs course. It enables on-GPU processing of computationally expensive calculations, such as data preprocessing and machine learning tasks, using libraries like cuDF and cuML. Additionally, RAPIDS minimizes CPU-GPU memory transfers by performing operations directly on the GPU, reducing latency and improving performance. Options A and B are identical and correct, reflecting RAPIDS' core functionality. Option C is incorrect, as RAPIDS does not focus on PCIe bus throughput or "word-doubling," which is not a relevant concept. Option D is wrong, as RAPIDS does not rely on lossless compression for acceleration but on GPU-parallel processing. The course notes: "RAPIDS accelerates data science by enabling GPU-based processing of computationally intensive tasks and minimizing CPU-GPU memory transfers, significantly speeding up workflows." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 72
Which calculation is most commonly used to measure the semantic closeness of two text passages?

A. Hamming distance
B. Cosine similarity
C. Jaccard similarity
D. Euclidean distance

Answer: B

Explanation:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 73
When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

A. Automatic parallelization of Python code across CPU cores.
B. Conversion of Pandas DataFrames to SQL tables for faster querying.
C. GPU-accelerated columnar data processing with zero-copy memory access.
D. Integration with cloud-based storage for distributed data access.

Answer: C

Explanation:
NVIDIA RAPIDS cuDF is a GPU-accelerated library that mimics Pandas' API but performs data manipulation on GPUs, significantly speeding up preprocessing tasks for LLM fine-tuning. The key feature enabling this performance is GPU-accelerated columnar data processing with zero-copy memory access, which allows cuDF to leverage the parallel processing power of GPUs and avoid unnecessary data transfers between CPU and GPU memory. According to NVIDIA's RAPIDS documentation, cuDF's columnar format and CUDA-based operations enable orders-of-magnitude faster data operations (e.g., filtering, grouping) compared to CPU-based Pandas. Option A is incorrect, as cuDF uses GPUs, not CPUs. Option C is false, as cloud integration is not a core cuDF feature. Option D is wrong, as cuDF does not rely on SQL tables.
References:
NVIDIA RAPIDS Documentation: https://rapids.ai/

NEW QUESTION # 74
What is 'chunking' in Retrieval-Augmented Generation (RAG)?

A. A technique used in RAG to split text into meaningful segments.
B. A method used in RAG to generate random text.
C. Rewrite blocks of text to fill a context window.
D. A concept in RAG that refers to the training of large language models.

Answer: A

Explanation:
Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM.
According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."

NEW QUESTION # 75
......

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