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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Multimodal AI Systems	- Multimodal model design - Cross-modal learning 1. Audio-visual understanding 2. Text-image integration
Responsible and Trustworthy AI	- Bias and safety considerations - Ethical AI principles
Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning
NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. GPU-accelerated AI workflows 2. NeMo framework usage

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NVIDIA Generative AI Multimodal NCA-GENM Prüfungsfragen mit Lösungen (Q41-Q46):

41. Frage
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A. Use a smaller vccabulary size.
B. Implement beam search with a larger beam width.
C. Decrease the learning rate of the model during training.
D. Increase the number of attention heads in the Transformer.
E. Increase the temperature parameter during text generation.

Antwort: E

Begründung:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.

42. Frage
Given the following Python code snippet utilizing spacy for text processing in a multimodal sentiment analysis pipeline, identify the potential issues and suggest improvements to enhance the accuracy and efficiency of the pipeline:

What improvements can be implemented?

A. The code doesn't account for the intensity of sentiment-bearing words. Introduce a weighting mechanism based on the part-of-speech tags to emphasize adjectives and adverbs.
B. The code doesn't handle contractions or special characters. Implement preprocessing steps to normalize the text before processing it with spacy.
C. The code uses the small spacy model, which might not be accurate for sentiment analysis. Use a larger model like 'en_core_web_lg' for better performance.
D. The code calculates sentiment based on individual tokens, ignoring context and negations. Integrate a sentiment analysis library like VADER or TextBlob for more accurate sentiment scoring.
E. Replace spacy entirely with NLTK for sentiment analysis, as it provides better pre-trained sentiment models.

Antwort: B,C,D

Begründung:
The small spacy model might lack the necessary vocabulary and training data for accurate sentiment analysis. The code's token- based sentiment calculation ignores context and negations, leading to inaccurate scoring. The code also needs preprocessing to handle contractions and special characters effectively. Sentiment analysis libraries provide more robust sentiment scoring mechanisms. Weighting by POS tags can help, but a better sentiment library is preferrable. Switching to NLTK entirely isn't necessarily better, upgrading the spacy model is a better option.

43. Frage
Consider this python code using PyTorch. What will be the output of the following code snippet, especially concerning the device used for computation?

A. Error: CUDA out of memory.
B. The program will execute successfully, but the speed will be the same no matter that CUDA is available
C. Tensor on CPU: False
D. Tensor on CPU: True
E. Runtime Error: No CUDA-capable device is available

Antwort: C

Begründung:
The torch.cuda.is_available() function checks if CUDA is available and torch.device sets the device to 'cuda' if available, otherwise 'cpu'. The tensor is created on the appropriate device and therefore 'my_tensor.is_cuda' will return true, thus the output will be 'Tensor on CPU: False'. Even if cuda is not available the programm will work on CPU.

44. Frage
Which of the following are potential benefits of using multi-modal learning compared to single-modal learning? (Select all that apply)

A. The ability to learn more comprehensive and nuanced representations.
B. Reduced risk of overfitting to spurious correlations in a single modality.
C. Increased computational complexity and data requirements.
D. Guaranteed higher accuracy across all tasks.
E. Improved robustness to noisy or incomplete data.

Antwort: A,B,E

Begründung:
Multi-modal learning leverages the complementary information from different modalities to enhance performance. (A) It improves robustness because if one modality is noisy or missing, the others can still provide useful information. (B) It learns more comprehensive representations by integrating information across modalities. (D) It reduces overfitting by leveraging information from multiple sources. (C) is correct but not a benefit. (E) is incorrect as higher accuracy is not guaranteed, depending on data and task.

45. Frage
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

A. Increasing the audio chunk size sent to the Riva server to reduce the number of requests.
B. Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.
C. Switching to a smaller, less accurate ASR model to reduce computational load.
D. Disabling automatic punctuation and capitalization to simplify the ASR process.
E. Deploying the Riva server on a CPU-only instance to reduce cost.

Antwort: B

Begründung:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.

46. Frage
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