100% Pass 2025 NVIDIA NCA-GENL: Updated NVIDIA Generative AI LLMs Exam Question

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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	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 6	Experiment Design
Topic 7	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 8	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.

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NVIDIA Generative AI LLMs Sample Questions (Q34-Q39):

NEW QUESTION # 34
Which metric is commonly used to evaluate machine-translation models?

A. Perplexity
B. BLEU score
C. ROUGE score
D. F1 Score

Answer: B

Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
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."

NEW QUESTION # 35
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Transfer learning
B. Dropout
C. Early stopping
D. Random initialization

Answer: A

Explanation:
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

NEW QUESTION # 36
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

A. Providing more memory for data analysis.
B. Ensuring that CPUs are running at full clock speed.
C. Using the GPU for parallel processing of data.
D. Enabling data processing to scale to multiple GPUs.
E. Subsampling datasets to provide rapid but approximate answers.

Answer: C,D

Explanation:
RAPIDS is an open-source suite of GPU-accelerated data science libraries developed by NVIDIA to speed up data processing and machine learning workflows. According to NVIDIA's RAPIDS documentation, its key advantages include:
* Option C: Using GPUs for parallel processing, which significantly accelerates computations for tasks like data manipulation and machine learning compared to CPU-based processing.
References:
NVIDIA RAPIDS Documentation:https://rapids.ai/

NEW QUESTION # 37
How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)

A. A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
B. A/B testing helps validate the impact of changes or updates to deep learning models bystatistically analyzing the outcomes of different versions to make informed decisions for model optimization.
C. A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.
D. A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.
E. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.

Answer: A,B

Explanation:
A/B testing is a controlled experimentation technique used to compare two versions of a system to determine which performs better. In the context of deep learning, NVIDIA's documentation on model optimization and deployment (e.g., Triton Inference Server) highlights its use in evaluating model performance:
* Option A: A/B testing validates changes (e.g., model updates or new features) by statistically comparing outcomes (e.g., accuracy or user engagement), enabling data-driven optimization decisions.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

NEW QUESTION # 38
What is a Tokenizer in Large Language Models (LLM)?

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

Answer: C

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 # 39
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100% Pass 2025 NVIDIA NCA-GENL: Updated NVIDIA Generative AI LLMs Exam Question

NVIDIA NCA-GENL Exam Syllabus Topics:

Vce NCA-GENL Exam | NCA-GENL Reliable Study Questions

NVIDIA Generative AI LLMs Sample Questions (Q34-Q39):

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