NCA-GENL無料問題集「NVIDIA Generative AI LLMs」

質問 1

When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

（A）Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.

（B）Subword tokenization removes punctuation and special characters to simplify text input.

（C）Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.

（D）Subword tokenization reduces the model's computational complexity by eliminating embeddings.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

（A）Use rule-based systems to manually define the characteristics of each category.

（B）Use a large, labeled dataset for each possible category.

（C）Train the new model from scratch for each new category encountered.

（D）Use a pre-trained language model with semantic embeddings.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

Why is layer normalization important in transformer architectures?

（A）To encode positional information within the sequence.

（B）To stabilize the learning process by adjusting the inputs across the features.

（C）To compress the model size for efficient storage.

（D）To enhance the model's ability to generalize to new data.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

（A）Increasing the model's parameter count to improve response quality.

（B）Reducing the input sequence length to minimize token processing.

（C）Enabling dynamic batching to process multiple requests simultaneously.

（D）Switching to a CPU-based inference engine for better scalability.

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

（A）Quantization might help in saving power and reducing heat production.

（B）Helps reduce memory requirements and achieve better cache utilization.

（C）It consists of removing a quantity of weights whose values are zero.

（D）It only involves reducing the number of bits of the parameters.

（E）It leads to a substantial loss of model accuracy.

正解：A、B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

"Hallucinations" is a term coined to describe when LLM models produce what?

（A）Images from a prompt description.

（B）Correct sounding results that are wrong.

（C）Grammatically incorrect or broken outputs.

（D）Outputs are only similar to the input data.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

Which principle of Trustworthy AI primarily concerns the ethical implications of AI's impact on society and includes considerations for both potential misuse and unintended consequences?

（A）Legal Responsibility

（B）Accountability

（C）Data Privacy

（D）Certification

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

Which of the following claims is correct about TensorRT and ONNX?

（A）TensorRT is used for model deployment and ONNX is used for model creation.

（B）TensorRT is used for model creation and ONNX is used for model interchange.

（C）TensorRT is used for model creation and ONNX is used for model deployment.

（D）TensorRT is used for model deployment and ONNX is used for model interchange.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

（A）Achieving the highest possible level of prediction accuracy in AI models.

（B）Ensuring AI systems have explicable decision-making processes.

（C）Developing AI systems with autonomy from human decision-making.

（D）Implementing complex algorithms to enhance AI's problem-solving capabilities.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

What do we usually refer to as generative AI?

（A）A branch of artificial intelligence that focuses on creating models that can generate new and original data.

（B）A branch of artificial intelligence that focuses on improving the efficiency of existing models.

（C）A branch of artificial intelligence that focuses on auto generation of models for classification.

（D）A branch of artificial intelligence that focuses on analyzing and interpreting existing data.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

NCA-GENL 無料問題集「NVIDIA Generative AI LLMs」

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