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NVIDIA NCA-GENM Exam Syllabus Topics:

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

>> NCA-GENM Examsfragen <<

Neueste NVIDIA Generative AI Multimodal Prüfung pdf & NCA-GENM Prüfung Torrent

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NVIDIA Generative AI Multimodal NCA-GENM Prüfungsfragen mit Lösungen (Q25-Q30):

25. Frage
What is a common method to reduce the computational cost of deep learning models during inference?

A. Increasing the batch size.
B. Adding more convolutional filters.
C. Pruning weights or neurons.
D. By replacing activation functions in some neurons with simpler ones.

Antwort: C

Begründung:
Pruning removes weights, neurons, or entire filters/channels that contribute minimally to model output - identified via magnitude-based criteria (removing near-zero weights), sensitivity analysis, or more sophisticated importance scoring - producing a smaller, sparser model that requires fewer computations and less memory at inference time while aiming to preserve accuracy through careful selection and, often, a fine- tuning step after pruning to recover any lost performance. Structured pruning (removing entire filters
/channels) yields hardware-friendly speedups on standard accelerators, while unstructured pruning (removing individual weights) achieves higher sparsity ratios but requires specialized sparse-computation hardware or libraries to realize actual speed gains.
The remaining options move in the wrong direction or address a different concern: adding more convolutional filters (B) increases model capacity and parameter count, which increases computational cost, the opposite of the stated goal. Increasing batch size (D) affects training throughput and memory usage per step but does not reduce the per-sample computational cost of inference - a larger batch does more total work, not less per inference call, and batch size at inference is often constrained by latency requirements rather than optimization goals. Option C's premise - selectively replacing activation functions with simpler ones in
"some neurons" - is not a standard or well-defined optimization technique; activation function choice is typically uniform within a layer and driven by training dynamics, not a piecemeal inference-cost lever.
Reference: Performance Optimization domain - pruning, alongside quantization and kernel fusion, as core inference-cost reduction techniques.

26. Frage
You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?

A. CycleGAN
B. Deep Convolutional GAN (DCGAN)
C. Vanilla GAN
D. Conditional GAN (cGAN)
E. Super-Resolution GAN (SRGAN)

Antwort: A

Begründung:
CycleGAN is designed for unpaired image-to-image translation. In this scenario, it can be adapted to translate between the image and text modalities without requiring paired data. One generator learns to generate images from text, while another learns to generate text from images. Cycle consistency ensures that translating an image to text and then back to an image results in an image similar to the original. Vanilla GANI cGAN, and DCGAN are not inherently designed for bi- directional translation between modalities without paired data. SRGAN is for image super-resolution.

27. Frage
Which metric is commonly used to evaluate machine-translation models?

A. F1 score
B. BLEU score
C. Accuracy
D. Mean Absolute Error (MAE)

Antwort: B

Begründung:
BLEU (Bilingual Evaluation Understudy) is the standard automatic metric for evaluating machine translation quality. It measures n-gram precision - the overlap of contiguous word sequences (unigrams through typically 4-grams) between the model's translated output and one or more human reference translations - combined with a brevity penalty to discourage overly short translations that could otherwise achieve artificially high precision. BLEU scores range from 0 to 1 (or 0-100 as a percentage), with higher scores indicating closer alignment to reference translations.
The distractors represent metrics standard to other task families: F1 score (A) evaluates classification tasks by balancing precision and recall over discrete positive/negative predictions, ill-suited to open-ended text generation where there is no fixed set of "correct" tokens. Accuracy (B) similarly assumes a discrete correct
/incorrect judgment, inappropriate for translation where multiple valid phrasings can convey the same meaning. Mean Absolute Error (C) is a regression metric measuring average magnitude of numeric prediction error, irrelevant to text output evaluation entirely.
It's worth noting BLEU has known limitations - it correlates imperfectly with human judgments of fluency and can penalize valid paraphrases - which has motivated complementary metrics like METEOR, ROUGE (more common for summarization), and learned metrics like BERTScore, though BLEU remains the benchmark most commonly referenced for translation specifically.
Reference: Core Machine Learning and AI Knowledge / Multimodal Data domains - task-specific evaluation metrics (BLEU for translation, WER for ASR, MOS for TTS).

28. Frage
You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A. Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.
B. Measuring inference throughput (samples per second).
C. Accuracy and F1-score on a held-out test set.
D. Calibration error, measuring the alignment between predicted probabilities and actual outcomes.
E. Robustness testing by introducing adversarial perturbations to the input data.

Antwort: A,D,E

Begründung:
Trustworthiness goes beyond simple accuracy. Calibration error assesses how well the model's predicted probabilities reflect the true likelihood of the diagnosis. Attribution methods provide insights into the model's reasoning process, helping to identify potential biases or reliance on irrelevant features. Robustness testing assesses the model's sensitivity to noise and adversarial attacks, which can indicate vulnerability to manipulation. Accuracy and Fl-score are important but insufficient for trustworthiness. Throughput is a performance metric, not a trustworthiness metric.

29. Frage
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Increase the frame rate of the training videos.
B. Decrease the resolution of the video frames.
C. Use a larger vocabulary for the text encoder.
D. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.
E. Using only pretrained model weights.

Antwort: D

Begründung:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

30. Frage
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