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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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 5	Experiment Design
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.

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NVIDIA Generative AI LLMs Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is a Tokenizer in Large Language Models (LLM)?

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

Answer: D

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 # 15
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

A. Simplified API for classical machine learning algorithms like SVM.
B. Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.
C. Automatic conversion of models to ONNX format for cross-platform deployment.
D. Built-in support for CPU-based data preprocessing pipelines.

Answer: B

Explanation:
The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA's TensorRT, enabling GPU-accelerated training and inference. NVIDIA's NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixed- precision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

NEW QUESTION # 16
What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

A. BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.
B. BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
C. BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.
D. BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.

Answer: A

Explanation:
BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are metrics used to evaluate natural language processing (NLP) models, particularly for tasks like machine translation and text summarization. According to NVIDIA's NeMo documentation on NLP evaluation metrics, BLEU primarily measures the precision of n-gram overlaps between generated and reference translations, making it suitable for assessing translation quality. ROUGE, on the other hand, focuses on recall, measuring the overlap of n-grams, longest common subsequences, or skip-bigrams between generated and reference summaries, making it ideal for summarization tasks. Option A is incorrect, as BLEU and ROUGE do not measure fluency or uniqueness directly. Option B is wrong, as both metrics focus on n-gram overlap, not syntactic or semantic analysis. Option D is false, as neither metric evaluates efficiency or complexity.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation." Lin, C.-Y. (2004). "ROUGE: A Package for Automatic Evaluation of Summaries."

NEW QUESTION # 17
When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

A. Zero-shot prompting with a generic task description.
B. Chain-of-thought prompting with step-by-step reasoning examples.
C. Retrieval-augmented generation with external mathematical databases.
D. Few-shot prompting with randomly selected examples.

Answer: B

Explanation:
Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model's (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness.
NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model's ability to mimic structured problem- solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning. Option A (zero-shot) is less effective for complex tasks due to lack of guidance. Option B (few- shot with random examples) is suboptimal without structured reasoning. Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."

NEW QUESTION # 18
In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

A. V is used to calculate the positional embeddings for each token in the input sequence.
B. Q, K, and V are randomly initialized weight matrices used for positional encoding.
C. Q represents the query vector used to retrieve relevant information from the input sequence.
D. K is responsible for computing the attention scores between the query and key vectors.

Answer: C

Explanation:
In the transformer architecture, the Q (query), K (key), and V (value) matrices are used in the self-attention mechanism to compute relationships between tokens in a sequence. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, the query vector (Q) represents the token seeking relevant information, the key vector (K) is used to compute compatibility with other tokens, and the value vector (V) provides the information to be retrieved. The attention score is calculated as a scaled dot- product of Q and K, and the output is a weighted sum of V. Option C is correct, as Q retrieves relevant information. Option A is incorrect, as Q, K, and V are not used for positional encoding. Option B is wrong, as attention scores are computed using both Q and K, not K alone. Option D is false, as positional embeddings are separate from V.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 19
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Free PDF 2025 NVIDIA NCA-GENL: Reliable Pass NVIDIA Generative AI LLMs Guarantee

NVIDIA NCA-GENL Exam Syllabus Topics:

Well-Prepared NVIDIA Pass NCA-GENL Guarantee Are Leading Materials & Accurate NCA-GENL: NVIDIA Generative AI LLMs

NVIDIA Generative AI LLMs Sample Questions (Q14-Q19):

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