NCA-GENM考古題介紹 - NCA-GENM證照指南

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>> NCA-GENM考古題介紹 <<

NCA-GENM證照指南，NCA-GENM試題

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最新的 NVIDIA-Certified Associate NCA-GENM 免費考試真題 (Q99-Q104):

問題 #99
Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

答案：A,B,C,D

解題說明：
Option A loads a small spacy model without word vectors. Option B loads the large spacy model with word vectors correctly, and calculates the similarity. Option C correctly loads word embeddings from a text file and uses cosine_similarity from sklearn.metrics.pairwise to get similarity Option D shows word similarity and usage of the gensim model.

問題 #100
Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?

A. Riva
B. Triton Inference Server
C. RAPIDS
D. Avatar Cloud Engine (ACE)
E. NeMo

答案：D

解題說明：
Avatar Cloud Engine (ACE) is specifically designed for building and deploying interactive avatars. It provides pre-built components and APIs for speech recognition, natural language understanding, and realistic avatar animation. Riva focuses primarily on speech AI, NeMo on training large language models, and Triton on model serving. RAPIDS is for data analytics and GPU-accelerated machine learning.

問題 #101
You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A. Using negative prompts to explicitly exclude unwanted features or styles.
B. Using short, concise prompts to minimize ambiguity.
C. Using prompts consisting only of keywords related to the desired image.
D. Using random prompts to explore the model's creative capabilities.
E. Using overly verbose and descriptive prompts to maximize detail.

答案：A

解題說明：
Negative prompting is a powerful technique where you specify what you don't want in the generated image. This helps refine the output and steer the model away from undesirable artifacts or styles. For example, specifying "a futuristic city, but without flying cars".

問題 #102
Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

A. Freeze the CNN, extract image features, and train the LLM to generate captions from these features.
B. Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.
C. Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.
D. Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.
E. Fine-tune both the CNN and the LLM jointly on the image captioning dataset.

答案：B,E

解題說明：
Fine-tuning both the CNN and LLM jointly allows the model to adapt both visual feature extraction and language generation to the specific task of image captioning, leading to potentially higher quality captions. However, this can be computationally expensive. Using a transformer-based encoder to process both modalities before the LLM decoder allows for effective cross-modal attention and fusion, which is also a strong approach. Freezing either the CNN or LLM limits the model's ability to adapt. Training separately and averaging outputs is unlikely to produce coherent captions.

問題 #103
You're training a Generative Adversarial Network (GAN) to generate images from text descriptions. After a few epochs, you notice the generator is producing nearly identical images regardless of the text input (mode collapse). Which of the following strategies could help mitigate this issue?

A. Decrease the learning rate of the discriminator.
B. Use a larger batch size for the generator.
C. Increase the capacity (number of layers/neurons) of the discriminator.
D. Apply weight decay regularization to the generator
E. Implement mini-batch discrimination in the discriminator.

答案：E

解題說明：
Mini-batch discrimination allows the discriminator to consider the entire batch of generated samples when making its decisiom This helps prevent the generator from finding a single 'shortcut' solution that fools the discriminator for individual samples, but fails to capture the diversity of the real data distribution, thus reducing the likelihood of mode collapse. Increasing discriminator capacity can also help, but mini-batch discrimination directly addresses the issue. Weight decay helps with generalization, but wont direclty combat mode collapse.

問題 #104
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