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NCA-GENL New Exam Materials Exam Pass at Your First Attempt | NVIDIA NCA-GENL: NVIDIA Generative AI LLMs

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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

Topic 4

Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.

Topic 5

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 6

Experiment Design

Topic 7

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 8

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 9

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.

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NVIDIA Generative AI LLMs Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is a Tokenizer in Large Language Models (LLM)?

A. A machine learning algorithm that predicts the next word/token in a sequence of text.
B. A tool used to split text into smaller units called tokens for analysis and processing.
C. A technique used to convert text data into numerical representations called tokens for machine learning.
D. A method to remove stop words and punctuation marks from text data.

Answer: B

Explanation:
A tokenizer in the context of large language models (LLMs) is a tool that splits text into smaller units called tokens (e.g., words, subwords, or characters) for processing by the model. NVIDIA's NeMo documentation on NLP preprocessing explains that tokenization is a critical step in preparing text data, with algorithms like WordPiece, Byte-Pair Encoding (BPE), or SentencePiece breaking text into manageable units to handle vocabulary constraints and out-of-vocabulary words. For example, the sentence "I love AI" might be tokenized into ["I", "love", "AI"] or subword units like ["I", "lov", "##e", "AI"]. Option A is incorrect, as removing stop words is a separate preprocessing step. Option B is wrong, as tokenization is not a predictive algorithm. Option D is misleading, as converting text to numerical representations is the role of embeddings, not tokenization.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 34
What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

A. Upgrade the GPU to a higher-end model.
B. Increase the clock speed of the CPU.
C. Using techniques like memory pooling.
D. Increase the number of CPU cores.

Answer: A,C

Explanation:
Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA's documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:
* Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html NVIDIA GPU Product Documentation:https://www.nvidia.com/en-us/data-center/products/

NEW QUESTION # 35
In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

A. Converting text to phonetic representations for cross-lingual alignment.
B. Removing all non-Latin characters to simplify the input.
C. Applying Unicode normalization to standardize character encodings.
D. Normalizing all text to a single script using transliteration.

Answer: C

Explanation:
When preparing a multilingual dataset for fine-tuning an LLM, applying Unicode normalization (e.g., NFKC or NFC forms) is the most effective preprocessing technique to handle text from diverse scripts like Latin, Cyrillic, or Devanagari. Unicode normalization standardizes character encodings, ensuring that visually identical characters (e.g., precomposed vs. decomposed forms) are represented consistently, which improves model performance across languages. NVIDIA's NeMo documentation on multilingual NLP preprocessing recommends Unicode normalization to address encoding inconsistencies in diverse datasets. Option A (transliteration) may lose linguistic nuances. Option C (removing non-Latin characters) discards critical information. Option D (phonetic conversion) is impractical for text-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

NEW QUESTION # 36
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

A. It is easier to explain the predictions.
B. Trained without the need for labeled data.
C. Single generic model can do more than one task.
D. Cheaper computational costs during inference.
E. Smaller latency, higher throughput.

Answer: C,D

Explanation:
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

NEW QUESTION # 37
How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

A. RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.
B. RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.
C. RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.
D. RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.

Answer: B

Explanation:
Retrieval-Augmented Generation (RAG) enhances trustworthy AI by generating responses that cite reference material from an external knowledge base, ensuring transparency and verifiability, as discussed in NVIDIA's Generative AI and LLMs course. RAG combines a retriever to fetch relevant documents with a generator to produce responses, allowing outputs to be grounded in verifiable sources, reducing hallucinations and improving trust. Option A is incorrect, as RAG does not focus on security features like confidential computing. Option B is wrong, as RAG is unrelated to energy efficiency. Option C is inaccurate, as RAG does not align models but integrates retrieved knowledge. The course notes: "RAG enhances trustworthy AI by generating responses with citations from external knowledge bases, improving transparency and verifiability of outputs." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 38
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