NVIDIA NCA-GENM Actual Exam, Valid Exam NCA-GENM Practice

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Multimodal Data	15%	- Applications and use cases - Handling and integrating text, image, and audio data
Topic 2: Data Analysis & Visualization	10%	- Visualization techniques for multimodal data - Data preprocessing and feature engineering
Topic 3: Software Development & Engineering	15%	- Integration and deployment of multimodal AI systems - Python libraries for multimodal AI
Topic 4: Core ML & AI Knowledge	20%	- Basic concepts and terminology - Key algorithms and techniques
Topic 5: Experimentation	25%	- Experimental design - Model evaluation and comparison - Hypothesis testing - A/B testing
Topic 6: Performance Optimization	10%	- Monitoring and improving system efficiency - Techniques for optimizing AI performance
Topic 7: Trustworthy AI	5%	- Ethical considerations in AI development - Ensuring fairness and transparency

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NVIDIA Generative AI Multimodal Sample Questions (Q14-Q19):

NEW QUESTION # 14
You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?

A. Replace ResNet50 with a larger, more complex model like ResNeXt101.
B. Disable GPU acceleration for image processing to reduce power consumption.
C. Use full precision floating point operations throughout the ResNet50 model.
D. Apply knowledge distillation, training a smaller, faster model to mimic the ResNet50 output.
E. Increase the batch size for image processing.

Answer: D

Explanation:
Knowledge distillation involves training a smaller, more efficient model to approximate the behavior of a larger, more accurate model. This can significantly reduce latency without a major drop in accuracy. Increasing batch size (A) may increase throughput but doesn't necessarily reduce latency per image. Replacing with a larger model (C) will increase latency and power consumption. Using full precision (D) is less energy-efficient than using mixed precision or quantization. Disabling GPU acceleration (E) would drastically increase latency.

NEW QUESTION # 15
A research team is developing a multimodal model to predict stock prices using financial news articles, company filings (text), historical stock prices (time-series), and executive interviews (audio). They are experiencing significant performance issues due to inconsistent data quality across modalities. What specific strategies would you recommend to address these data quality challenges?

A. Apply Named Entity Recognition (NER) to financial news and company filings to standardize company names and financial terms.
B. All of the above.
C. Normalize and scale historical stock prices to a consistent range to avoid dominance by high-magnitude values.
D. Implement audio transcription and sentiment analysis on executive interviews to extract key information and emotional tone.
E. Focus exclusively on improving the quality of the most readily available data source.

Answer: B

Explanation:
All the options are essential. NER standardizes textual data, audio analysis extracts sentiment, and normalization prevents stock price dominance. Addressing data quality holistically across modalities is key.

NEW QUESTION # 16
You're building a generative A1 model that can create realistic 3D models from text descriptions. You have a dataset of text descriptions and corresponding 3D models, but the alignment between the text and the 3D models is weak. The model sometimes generates 3D shapes that don't accurately reflect the text. Which of the following techniques could improve the alignment between the text descriptions and the generated 3D models?

A. Training the model with a larger batch size.
B. Increasing the number of vertices and faces in the 3D models.
C. Using a pre-trained text encoder (e.g., BERT or CLIP) to extract meaningful features from the text descriptions.
D. Using a contrastive loss function that encourages the model to generate 3D models that are semantically similar to the corresponding text descriptions.
E. Applying data augmentation techniques to the 3D models (e.g., random rotations and scaling).

Answer: C,D

Explanation:
A contrastive loss function directly encourages the model to learn a mapping between text and 3D models that preserves semantic similarity. Using a pre-trained text encoder allows the model to leverage existing knowledge about language and extract more meaningful features from the text descriptions, improving alignment. Increasing the number of vertices and faces can improve the resolution of the models but won't directly address alignment. 3D data augmentation can improve robustness, but it's less direct. Batch size has a smaller impact compared to the other options.

NEW QUESTION # 17
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)

A. Increase the learning rate for the audio encoder.
B. Apply modality-specific dropout to the text encoder.
C. Use a contrastive loss function that encourages alignment between text and audio representations.
D. Reduce the size of the text encoder.
E. Replace audio features with raw audio waveform.

Answer: B,C

Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.

NEW QUESTION # 18
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 # 19
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