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NVIDIA Generative AI LLMs Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which of the following optimizations are provided by TensorRT? (Choose two.)

• A. Residual connections
• B. Layer Fusion
• C. Variable learning rate
• D. Data augmentation
• E. Multi-Stream Execution

Answer: B,E

Explanation:
NVIDIA TensorRT provides optimizations to enhance the performance of deep learning models during inference, as detailed in NVIDIA's Generative AI and LLMs course. Two key optimizations are multi-stream execution and layer fusion. Multi-stream execution allows parallel processing of multiple input streams on the GPU, improving throughput for concurrent inference tasks. Layer fusion combines multiple layers of a neural network (e.g., convolution and activation) into a single operation, reducing memory access and computation time. Option A, data augmentation, is incorrect, as it is a preprocessing technique, not a TensorRT optimization. Option B, variable learning rate, is a training technique, not relevant to inference. Option E, residual connections, is a model architecture feature, not a TensorRT optimization. The course states:
"TensorRT optimizes inference through techniques like layer fusion, which combines operations to reduce overhead, and multi-stream execution, which enables parallel processing for higher throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 12
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

• A. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
• B. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
• C. ReLU is a linear function while sigmoid is non-linear.
• D. ReLU and sigmoid both have a range of 0 to 1.

Answer: A

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