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If you want to pass a high percentage of the NVIDIA NCA-GENM Exam, you should consider studying for the actual exam. These practice tests are designed to help you prepare for the exam and ensure you know the syllabus content. It will also help you improve your time management skills, as these tests are designed like an actual exam. Moreover, they will help you learn to answer all questions in the time allowed.
| Section | Objectives |
|---|---|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Generative AI Concepts | - Generative models
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
>> Latest NCA-GENM Exam Questions Vce <<
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NEW QUESTION # 43
You are tasked with evaluating a text-to-video generation model. Which of the following metrics would be MOST appropriate for assessing the temporal coherence and smoothness of the generated videos?
Answer: B
Explanation:
Frchet Video Distance (FVD) is a metric specifically designed for evaluating video generation models. It extends the concept of FID to the video domain by comparing the distributions of features extracted from real and generated videos, taking into account the temporal dimension. IS and FID are primarily used for image generation. LPIPS measures the perceptual similarity between two images, and BLEU score is used for evaluating text generation.
NEW QUESTION # 44
You're building a multimodal model that takes an image and a question as input and outputs an answer (Visual Question Answering - VQA). You find your model is heavily relying on the question type (e.g., 'What color is...' always predicts 'blue') and ignoring the image content. Select TWO of the following techniques that could help mitigate this 'language prior' problem.
Answer: A,B
Explanation:
B and D are the best answers. Using a question-only baseline (B) allows you to directly quantify the model's reliance on language priors and then penalize the model for over-relying on them during training, encouraging it to pay more attention to the image. Balancing the dataset (D) by ensuring an equal number of correct answers for each question type makes it harder for the model to simply predict based on the question type alone. The image encoder shouldn't be replaced as that is needed in the task. More images wouldn't necessarily fix the data imbalance.
NEW QUESTION # 45
You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?
Answer: C
Explanation:
Exporting the NeMo model to ONNX (Open Neural Network Exchange) is essential for compatibility with Triton Inference Server and TensorRT optimization. ONNX provides a standard format that TensorRT can ingest and optimize for efficient inference on NVIDIA GPUs.
NEW QUESTION # 46
Which prompt engineering technique is most likely to improve the coherence and visual quality of images generated by a text-to-image model when generating complex scenes with multiple objects and intricate details?
Answer: C
Explanation:
Employing a negative prompt allows you to explicitly instruct the model to avoid specific elements or artifacts that might degrade the visual quality of the generated image. This technique is particularly effective for complex scenes where you want to fine-tune the composition and appearance by preventing unwanted details or styles. Using shorter prompts or relying only on default styles is too general. Vague descriptions can lead to unpredictable outputs.
NEW QUESTION # 47
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?
Answer: A,C,D,E
Explanation:
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.
NEW QUESTION # 48
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