NCA-GENL EXAMCOLLECTION DUMPS | LATEST NCA-GENL TEST PASS4SURE

NCA-GENL Examcollection Dumps | Latest NCA-GENL Test Pass4sure

NCA-GENL Examcollection Dumps | Latest NCA-GENL Test Pass4sure

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

NEW QUESTION # 34
Why do we need positional encoding in transformer-based models?

  • A. To increase the throughput of the model.
  • B. To represent the order of elements in a sequence.
  • C. To reduce the dimensionality of the input data.
  • D. To prevent overfitting of the model.

Answer: B

Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
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 # 35
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. Retrieval-augmented generation with external mathematical databases.
  • B. Few-shot prompting with randomly selected examples.
  • C. Zero-shot prompting with a generic task description.
  • D. Chain-of-thought prompting with step-by-step reasoning examples.

Answer: D

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 # 36
What is a Tokenizer in Large Language Models (LLM)?

  • A. A method to remove stop words and punctuation marks from text data.
  • 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 machine learning algorithm that predicts the next word/token in a sequence of text.

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 # 37
In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

  • A. Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.
  • B. Multi-head attention simplifies the training process by reducing the number of parameters.
  • C. Multi-head attention reduces the model's memory footprint by sharing weights across heads.
  • D. Multi-head attention eliminates the need for positional encodings in the input sequence.

Answer: A

Explanation:
Multi-head attention, a core component of the transformer architecture, improves model performance by allowing the model to attend to multiple aspects of the input sequence simultaneously. Each attention head learns to focus on different relationships (e.g., syntactic, semantic) in the input, capturing diverse contextual dependencies. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, multi-head attention enhances the expressive power of transformers, making them highly effective for complex NLP tasks like translation or question-answering. Option A is incorrect, as multi-head attention increases memory usage. Option C is false, as positional encodings are still required. Option D is wrong, asmulti-head attention adds parameters.
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 # 38
Which of the following best describes the purpose of attention mechanisms in transformer models?

  • A. To compress the input sequence for faster processing.
  • B. To convert text into numerical representations.
  • C. To focus on relevant parts of the input sequence for use in the downstream task.
  • D. To generate random noise for improved model robustness.

Answer: C

Explanation:
Attention mechanisms in transformer models, as introduced in "Attention is All You Need" (Vaswani et al.,
2017), allow the model to focus on relevant parts of the input sequence by assigning higher weights to important tokens during processing. NVIDIA's NeMo documentation explains that self-attention enables transformers to capture long-range dependencies and contextual relationships, making them effective for tasks like language modeling and translation. Option B is incorrect, as attention does not compress sequences but processes them fully. Option C is false, as attention is not about generating noise. Option D refers to embeddings, not attention.
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 # 39
......

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