2026 Free NVIDIA NCA-GENM Exam Files Downloaded Instantly [Q34-Q49]

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2026 Free NVIDIA NCA-GENM Exam Files Downloaded Instantly

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

Q34. Which framework is used for conversational AI models development?

NVIDIA Metropolis

NVIDIA NeMo

NVIDIA DeepStream

NVIDIA Clara

Q35. You’re building a real-time voice cloning application using NVIDIA Riv
a. You need to ensure high-quality synthesized speech with minimal latency. Which of the following Riva configurations would provide the BEST trade-off between quality and speed?

Using a large, high-capacity Tacotron 2 text-to-speech model and a high-resolution WaveGlow vocoder, deployed on a single, low-power GPU.

Using a smaller, faster FastSpeech text-to-speech model and a parallel WaveGAN vocoder, deployed on a multi-GPU server with TensorRT optimization enabled.

Using a pre-trained, open-source text-to-speech model and a CPU-based vocoder, optimized for minimal memory footprint.

Using a large, transformer-based text-to-speech model with aggressive quantization and pruning, deployed on a cloud-based TPIJ instance.

Using only the open source implementation and not NVIDIA Riva to implement a Voice Cloning application

Q36. You are building a multimodal model that takes images and text descriptions as input to generate new images. You want to evaluate the impact of different image encoders (ResNet50, Efficient Net) on the generated image quality and relevance to the text prompt. Which evaluation metric(s) would be MOST appropriate for this task?

Inception Score (IS) only

Frechet Inception Distance (FID) only

CLIP Score only

Both FID and CLIP Score

Perplexity and BLEU score

Q37. You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?

Instance Group Count

Default Model Filename

Max Queue Size

Batching Preferences

Input Data Type

Q38. You are fine-tuning a pre-trained multimodal model for a visual question answering (VQA) task. You notice that the model performs well on common questions but struggles with questions requiring reasoning about object relationships (e.g., ‘Is the object to the left of the table bigger than the one on the table?’). What data augmentation technique would MOST likely improve performance on these challenging questions?

Randomly cropping the images.

Adding Gaussian noise to the images.

Replacing objects in the images with other objects.

Generating synthetic images with varying object arrangements and relationships, paired with corresponding questions and answers.

Rotating the images.

Q39. You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?

Increasing the number of layers in the CNN to extract more detailed visual features.

Incorporating a Transformer-based architecture, such as a Vision Transformer (ViT) for visual feature extraction and a standard Transformer for sequence generation.

Reducing the frame rate of the input video to reduce the temporal complexity.

Using a smaller batch size during training to reduce memory consumption.

Q40. In experimentation, how does data augmentation contribute to improving model accuracy?

It helps in increasing the size of the dataset, leading to better generalization of the model.

It reduces the complexity of the model, making it easier to train and evaluate.

It has no impact on model accuracy and is primarily used for data visualization purposes.

It improves the interpretability of the model by providing additional insights into the data.

Q41. You’re training a Generative Adversarial Network (GAN) to generate realistic images of faces. After several epochs, you notice that the generator is producing very similar faces, lacking diversity. Which of the following techniques could BEST address this mode collapse issue?

Decrease the learning rate of the discriminator.

Increase the batch size used for training the generator

Implement Minibatch Discrimination in the discriminator.

Use a simpler generator architecture.

Reduce the noise injected into the generator’s input.

Q42. You are developing a multimodal model that combines time-series data from sensor readings with natural language descriptions of events. The time-series data has varying sampling rates and the text descriptions are often vague and ambiguous. How would you best address the challenge of aligning and fusing these two modalities to improve model performance?

Resample the time-series data to a uniform sampling rate and directly concatenate it with the text embeddings.

Ignore the time-series data and train the model only on the text descriptions.

Use a dynamic time warping (DTW) algorithm to align the time-series data with the text descriptions and then use a cross-modal attention mechanism for fusion.

Average the time-series data over a fixed time window and concatenate it with the text embeddings.

Train separate models for time-series and text and average their predictions.

Q43. When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)

Prioritize model accuracy over inference speed.

Employ model quantization and pruning techniques to reduce model size and computational requirements.

Utilize asynchronous processing and caching strategies to pre-compute and store frequently accessed data.

Deploy the model on a single CPU core to minimize resource contention.

Disable any logging or monitoring to reduce overhead.

Q44. You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model’s performance?

Directly feeding the raw clinical notes into the model without any preprocessing.

Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).

Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.

Translating the clinical notes into multiple languages and then back-translating to the original language.

Performing sentiment analysis on the clinical notes.

Q45. You’re analyzing the performance of a generative A1 model that produces images from text prompts. You notice that the model struggles to generate images with specific objects mentioned in the prompt, even though these objects appear frequently in the training dataset.
Which of the following techniques could BEST address this issue?

Increasing the batch size during training.

Implementing a re-weighting scheme that gives higher weight to training examples containing the problematic objects.

Reducing the learning rate during training.

Adding more layers to the generative model’s architecture.

Applying a larger kernel size in the convolutional layers of the model.

Q46. Which of the following are potential benefits of using multi-modal learning compared to single-modal learning? (Select all that apply)

Improved robustness to noisy or incomplete data.

The ability to learn more comprehensive and nuanced representations.

Increased computational complexity and data requirements.

Reduced risk of overfitting to spurious correlations in a single modality.

Guaranteed higher accuracy across all tasks.

Q47. You’re building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot’s performance on complex visual scenes?

Use a larger language model for the chatbot.

Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.

Reduce the resolution of the input images.

Train the chatbot on a dataset with only simple images containing a single object.

Remove the image processing component entirely.

Q48. You are working on a multimodal A1 model that translates spoken language from one language (e.g., English) to another (e.g., Spanish) while also generating a corresponding visual representation of the translated sentence. You have access to a large dataset of parallel spoken language and image pairs, but the image quality is highly variable. Some images are clear and detailed, while others are blurry and noisy. How should you best handle the data to build the multimodal system?

Train the model solely on the high-quality images, discarding the low-quality images.

Train the model using all images, but apply a higher weight to the loss function for high-quality images.

Use a pre-trained image enhancement model to improve the quality of the low-quality images before training the multimodal model.

Implement a curriculum learning approach, starting with high-quality images and gradually introducing low-quality images as the model improves.

Ignore image data entirely and train only with spoken language.

Q49. You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

Increase the batch size used for training.

Use a simpler generator architecture.

Implement a perceptual loss function that compares high-level features of generated and real images.

Decrease the learning rate of the discriminator.

Train the model for fewer epochs.

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NVIDIA NCA-GENM Exam Syllabus Topics:

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