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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
- 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 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
- 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 5
- 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.
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NVIDIA Generative AI LLMs Sample Questions (Q17-Q22):
NEW QUESTION # 17
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 focus on relevant parts of the input sequence for use in the downstream task.
- C. To generate random noise for improved model robustness.
- D. To convert text into numerical representations.
Answer: B
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 # 18
Which calculation is most commonly used to measure the semantic closeness of two text passages?
- A. Cosine similarity
- B. Euclidean distance
- C. Hamming distance
- D. Jaccard similarity
Answer: A
Explanation:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
NEW QUESTION # 19
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)
- A. It only involves reducing the number of bits of the parameters.
- B. It consists of removing a quantity of weights whose values are zero.
- C. Helps reduce memory requirements and achieve better cache utilization.
- D. Quantization might help in saving power and reducing heat production.
- E. It leads to a substantial loss of model accuracy.
Answer: C,D
Explanation:
Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32- bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA's documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
* Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
References:
NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
NEW QUESTION # 20
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?
- A. ReLU and sigmoid both have a range of 0 to 1.
- B. ReLU is a linear function while sigmoid is non-linear.
- C. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
- D. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
Answer: D
Explanation:
ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA's deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e
