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Exam NCA-GENL braindumps

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NVIDIA Generative AI LLMs Sample Questions (Q27-Q32):

NEW QUESTION # 27
In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

A. To automate the collection of training data for fine-tuning.
B. To optimize the model's inference speed for production deployment.
C. To identify and mitigate potential biases, safety risks, and harmful outputs.
D. To increase the model's parameter count for better performance.

Answer: C

Explanation:
Red-teaming exercises involve systematically testing a large language model (LLM) by probing it with adversarial or challenging inputs to uncover vulnerabilities, such as biases, unsafe responses, or harmful outputs. NVIDIA's Trustworthy AI framework emphasizes red-teaming as a critical stepin the alignment process to ensure LLMs adhere to ethical standards and societal values. By simulating worst-case scenarios, red-teaming helps developers identify and mitigate risks, such as generating toxic content or reinforcing stereotypes, before deployment. Option A is incorrect, as red-teaming focuses on safety, not speed. Option C is false, as it does not involve model size. Option D is wrong, as red-teaming is about evaluation, not data collection.
References:
NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 28
In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

A. It reduces the computational complexity by normalizing the input embeddings.
B. It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.
C. It increases the model's capacity by adding additional parameters to each layer.
D. It replaces the attention mechanism to improve sequence processing efficiency.

Answer: B

Explanation:
Layer normalization is a technique used in transformer-based large language models (LLMs) to stabilize and accelerate training by normalizing the inputs to each layer. According to the original transformer paper ("Attention is All You Need," Vaswani et al., 2017) and NVIDIA's NeMo documentation, layer normalization reduces internal covariate shift by ensuring that the mean andvariance of activations remain consistent across layers, mitigating issues like vanishing or exploding gradients in deep networks. This is particularly crucial in transformers, which have many layers and process long sequences, making them prone to training instability. By normalizing the activations (typically after the attention and feed-forward sub- layers), layer normalization improves gradient flow and convergence. Option A is incorrect, as layer normalization does not reduce computational complexity but adds a small overhead. Option C is false, as it does not add significant parameters. Option D is wrong, as layer normalization complements, not replaces, the attention mechanism.
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 # 29
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 method to remove stop words and punctuation marks from text data.
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 # 30
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

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

Answer: D,E

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 # 31
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

A. Few-shot prompting with unrelated examples.
B. Chain-of-thought prompting with explicit intermediate steps.
C. Retrieval-augmented generation without context
D. Zero-shot prompting with detailed task descriptions.

Answer: B

Explanation:
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
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 # 32
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