NCA-GENM exam questions: NVIDIA Generative AI Multimodal & NCA-GENM study materials

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: Multimodal AI Systems	- Cross-modal learning 1. Audio-visual understanding 2. Text-image integration - Multimodal model design
Topic 2: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows
Topic 3: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Supervised and unsupervised learning 2. Neural networks fundamentals
Topic 4: Responsible and Trustworthy AI	- Bias and safety considerations - Ethical AI principles
Topic 5: Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models

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Hot Dumps NCA-GENM Questions | Professional NVIDIA NCA-GENM Certification Practice: NVIDIA Generative AI Multimodal

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NVIDIA Generative AI Multimodal Sample Questions (Q32-Q37):

NEW QUESTION # 32
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)

A. Self-BLEU
B. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
C. SPICE (Semantic Propositional Image Caption Evaluation)
D. CIDEr (Consensus-based Image Description Evaluation)
E. BLEU (Bilingual Evaluation Understudy)

Answer: A

Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.

NEW QUESTION # 33
You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

A. Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.
B. Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
C. Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.
D. Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.
E. Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.

Answer: C

Explanation:
Using word embeddings captures semantic meaning in text. Pre-trained CNN features provide high-level image representations. Graph embedding for user profiles captures relationships between users. These advanced techniques provide better representations than simple methods like TF-IDF, pixel values, or bag-of-words.

NEW QUESTION # 34
You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.

A. Use a reinforcement learning approach where the reward is based on whether the generated code passes the unit tests associated with the descriptiom
B. Increase the number of Transformer layers.
C. Fine-tune the model on a subset of the data where each code snippet is accompanied by unit tests, and train the model to also generate these tests.
D. Increase the size of the vocabulary used by the model.
E. Apply techniques like beam search during decoding to generate more diverse code snippets.

Answer: A,C

Explanation:
Fine-tuning with unit tests (B) and using reinforcement learning with unit test rewards (D) directly address semantic correctness- The model learns to generate code that not only compiles but also behaves as expected- Increasing Transformer layers (A) or vocabulary size (E) might improve syntactic correctness or fluency, but don't guarantee semantic accuracy Beam search (C) generates more diverse outputs, but doesn't explicitly guide the model towards semantic correctness.

NEW QUESTION # 35
Consider a scenario where you are training a multimodal Generative A1 model using both image and text dat a. The image data is stored in a directory with millions of high-resolution images, and the text data is in a large CSV file. What is the MOST efficient way to load and preprocess this data for training, minimizing memory usage and maximizing throughput?

A. Resize all images to a very small size before loading them into memory.
B. Use a distributed file system to store the data and load it directly into the model during training.
C. Convert all images to a single large video file and load it into memory along with the text data.
D. Load all images and text data into memory at once, preprocess them, and then feed them to the model.
E. Use a data generator (e.g., TensorFlow's 'tf.data.Dataset' or PyTorch's *DataLoadeN) to load and preprocess data in batches.

Answer: E

Explanation:
Data generators are the most efficient way to handle large datasets because they load and preprocess data in batches, minimizing memory usage. Option A is infeasible for large datasets. Option C is not a standard or efficient approach. Option D is relevant for distributed training but doesn't address the memory issue of loading the entire dataset. Option E reduces memory usage but may sacrifice important image details.

NEW QUESTION # 36
Which of the following techniques are MOST relevant to optimizing the energy efficiency of a large multimodal generative A1 model deployed on NVIDIA GPUs? (Select TWO)

A. Implementing model parallelism across multiple GPUs without optimizing communication overhead.
B. Increasing the size of the hidden layers in the transformer architecture.
C. Knowledge distillation, transferring the knowledge to a smaller model.
D. Adding more data augmentation techniques to the training process.
E. Using mixed precision training (e.g., FP16) to reduce memory usage and computation.

Answer: C,E

Explanation:
Knowledge distillation reduces the model size, directly decreasing energy consumption. Mixed precision training reduces memory bandwidth and computation, leading to significant energy savings. Increasing hidden layer size increases computational cost and energy consumption. Data augmentation can improve accuracy, but not energy efficiency. Poorly implemented model parallelism can increase communication overhead, negating any energy savings.

NEW QUESTION # 37
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