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NCA-GENL認證考試 - NCA-GENL證照信息

我們VCESoft NVIDIA的NCA-GENL考題是的100%通過驗證和測試的，是通過認證的專家，我們VCESoft NVIDIA 的NCA-GENL的考試練習題及答案是通過實踐檢驗的軟體和它最終的認證準備培訓工具。在VCESoft中，你會發現最好的認證準備資料，這些資料包括練習題及答案，我們的資料有機會讓你實踐問題，最終實現自己的目標通過 NVIDIA的NCA-GENL考試認證。

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>> NCA-GENL認證考試 <<

免費下載NCA-GENL認證考試 & NVIDIA Generative AI LLMs證照信息

我們VCESoft NVIDIA的NCA-GENL考試的做法是最徹底的，以及最準確及時的最新的實踐檢驗，你會發現目前市場上的唯一可以有讓你第一次嘗試通過困難的信心。NVIDIA的NCA-GENL考試認證在世界上任何一個國家將會得到承認，所有的國家將會一視同仁，VCESoft NVIDIA的NCA-GENL認證證書不僅有助於提高你的知識和技能，也有助於你的職業生涯在不同的條件下多出一個可能性，我們VCESoft NVIDIA的NCA-GENL考試認證合格使用。

最新的 NVIDIA-Certified Associate NCA-GENL 免費考試真題 (Q23-Q28):

問題 #23
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Random initialization
B. Transfer learning
C. Early stopping
D. Dropout

答案：B

解題說明：
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

問題 #24
In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

A. It increases the model's capacity by adding additional parameters to each layer.
B. It replaces the attention mechanism to improve sequence processing efficiency.
C. It reduces the computational complexity by normalizing the input embeddings.
D. It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.

答案：D

解題說明：
Layer normalization is a technique used in transformer-based large language models (LLMs) to stabilize and accelerate training by normalizing the inputs to each layer. According to the original transformer paper ("Attention is All You Need," Vaswani et al., 2017) and NVIDIA's NeMo documentation, layer normalization reduces internal covariate shift by ensuring that the mean andvariance of activations remain consistent across layers, mitigating issues like vanishing or exploding gradients in deep networks. This is particularly crucial in transformers, which have many layers and process long sequences, making them prone to training instability. By normalizing the activations (typically after the attention and feed-forward sub- layers), layer normalization improves gradient flow and convergence. Option A is incorrect, as layer normalization does not reduce computational complexity but adds a small overhead. Option C is false, as it does not add significant parameters. Option D is wrong, as layer normalization complements, not replaces, the attention mechanism.
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

問題 #25
What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

A. BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
B. BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.
C. BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.
D. BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.

答案：D

解題說明：
BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are metrics used to evaluate natural language processing (NLP) models, particularly for tasks like machine translation and text summarization. According to NVIDIA's NeMo documentation on NLP evaluation metrics, BLEU primarily measures the precision of n-gram overlaps between generated and reference translations, making it suitable for assessing translation quality. ROUGE, on the other hand, focuses on recall, measuring the overlap of n-grams, longest common subsequences, or skip-bigrams between generated and reference summaries, making it ideal for summarization tasks. Option A is incorrect, as BLEU and ROUGE do not measure fluency or uniqueness directly. Option B is wrong, as both metrics focus on n-gram overlap, not syntactic or semantic analysis. Option D is false, as neither metric evaluates efficiency or complexity.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation." Lin, C.-Y. (2004). "ROUGE: A Package for Automatic Evaluation of Summaries."

問題 #26
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

A. Cheaper computational costs during inference.
B. It is easier to explain the predictions.
C. Trained without the need for labeled data.
D. Single generic model can do more than one task.
E. Smaller latency, higher throughput.

答案：A,D

解題說明：
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

問題 #27
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

答案：B

解題說明：
Accountability is a core principle of Trustworthy AI that addresses the ethical implications of AI's societal impact, including potential misuse and unintended consequences. NVIDIA's guidelines on Trustworthy AI, as outlined in their AI ethics framework, emphasize accountability as ensuring that AI systems are transparent, responsible, and answerable for their outcomes. This includes mitigating risks of bias, ensuring fairness, and addressing unintended societal impacts. Option A (Certification) refers to compliance processes, not ethical implications. Option B (Data Privacy) focuses on protecting user data, not broader societal impact. Option D (Legal Responsibility) is related but narrower, focusing on liability rather than ethical considerations.
References:
NVIDIA Trustworthy AI:https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

問題 #28
......

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NCA-GENL證照信息: https://www.vcesoft.com/NCA-GENL-pdf.html

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