최신 NCA-GENL 무료덤프 - NVIDIA Generative AI LLMs

문제1

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 optimize the model's inference speed for production deployment.

B. To increase the model's parameter count for better performance.

C. To identify and mitigate potential biases, safety risks, and harmful outputs.

D. To automate the collection of training data for fine-tuning.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제2

When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

A. ReLU is a linear function while sigmoid is non-linear.

B. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.

C. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.

D. ReLU and sigmoid both have a range of 0 to 1.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제3

Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

A. Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.

B. Simplified API for classical machine learning algorithms like SVM.

C. Built-in support for CPU-based data preprocessing pipelines.

D. Automatic conversion of models to ONNX format for cross-platform deployment.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제4

When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

A. Conversion of Pandas DataFrames to SQL tables for faster querying.

B. Automatic parallelization of Python code across CPU cores.

C. Integration with cloud-based storage for distributed data access.

D. GPU-accelerated columnar data processing with zero-copy memory access.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제5

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 generate random noise for improved model robustness.

C. To focus on relevant parts of the input sequence for use in the downstream task.

D. To convert text into numerical representations.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

최신 NCA-GENL 무료덤프 - NVIDIA Generative AI LLMs

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