2026 Updated Verified Pass NCA-GENM Exam – Real Questions & Answers [Q186-Q200]

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2026 Updated Verified Pass NCA-GENM Exam – Real Questions and Answers

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NEW QUESTION 186
You are deploying a multimodal generative A1 model using Triton Inference Server. The model takes both image and text inputs. Which of the following approaches is most suitable for handling the preprocessing and postprocessing steps within Triton?

Performing all preprocessing and postprocessing on the client-side before sending the data to Triton and after receiving the results.

Implementing the preprocessing and postprocessing logic within the model itself as part of the neural network architecture.

Using Triton’s ensemble models to chain preprocessing, the core generative model, and postprocessing models together.

Writing custom C++ code to handle preprocessing and postprocessing within Triton’s backend.

Relying solely on Triton’s automatic data type conversion capabilities without implementing any explicit preprocessing or postprocessing.

Triton’s ensemble models provide the most flexible and scalable way to handle preprocessing and postprocessing. By creating separate models for these steps and chaining them together with the core generative model, you can easily manage complex pipelines and optimize each stage independently. Client-side processing (A) increases client burden. Embedding logic in the model (B) limits flexibility. Custom C++ code (D) is complex. Relying solely on automatic conversion (E) is often insufficient.

NEW QUESTION 187
Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

Image segmentation and pixel-wise classification.

Image inpainting and super-resolution tasks.

Encoding high-dimensional text data for multimodal embeddings.

Medical image analysis, such as tumor detection.

Facilitating efficient feature extraction and upsampling for detailed image generation.

U-Nets are primarily used for image-to-image tasks like segmentation, inpainting, and super-resolution. They excel at processing and generating images, but are not directly involved in encoding text data. CLIP is used for that purpose.

NEW QUESTION 188
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.

Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.

Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.

Reduce the size of the model to prevent overfitting to the training data.

Only evaluate on a subset of the test data that closely resembles the training data.

Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.

NEW QUESTION 189
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.

Re-weighting the training data to emphasize examples containing the underrepresented objects directly addresses the issue of the model not learning these objects effectively. Increasing batch size or adding layers might improve overall performance but doesn’t specifically target the object representation problem. Reducing learning rate may lead to slower convergence. Increasing kernel size changes receptive field but does not address the imbalanced representation of objects.

NEW QUESTION 190
You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

Fine-tuning the entire model with a small learning rate.

Freezing all layers of the pre-trained model and training only a small classification head.

Using quantization to reduce the precision of the model’s weights and activations.

Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.

Increasing the batch size to utilize the available memory more efficiently.

Quantization reduces the memory footprint of the model by using lower-precision representations for weights and activations. Knowledge distillation allows you to train a smaller, more efficient model that performs similarly to the larger pre-trained model. Freezing layers reduces the number of trainable parameters but may limit the model’s ability to adapt to the new task. Fine-tuning the entire model, even with a small learning rate, is computationally expensive. Increasing batch size might lead to Out of Memory errors.

NEW QUESTION 191
You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?

Fine-tune only the classification head (output layer) while freezing all other layers of the pre-trained model.

Fine-tune all layers of the pre-trained model with a very small learning rate.

Fine-tune a subset of layers, specifically those responsible for feature extraction from both image and text modalities, while keeping the lower layers frozen.

Train a new model from scratch on the new task’s dataset.

Use knowledge distillation to transfer knowledge from the pre-trained model to a smaller, more efficient model.

Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model’s architecture is suitable.

NEW QUESTION 192
You’ve trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model’s factual accuracy and coherence?

Increasing the model size by adding more layers.

Implementing a retrieval-augmented generation (RAG) approach.

Reducing the temperature parameter during generation.

Training the model on a smaller dataset.

Removing dropout layers.

Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model’s generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn’t guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn’t address factual correctness. Smaller datasets and removing dropout would likely worsen performance.

NEW QUESTION 193
Consider a multimodal generative model trained on a dataset of images and corresponding captions. After training, you observe that the model generates captions that are grammatically correct but often lack specific details and relevance to the input image. Which of the following regularization techniques is MOST likely to improve the faithfulness and informativeness of the generated captions?

L1 regularization on the model weights.

Dropout during training.

KL divergence regularization to encourage the generated caption distribution to be similar to the prior caption distribution.

Attention regularization to encourage the model to attend to relevant regions in the image when generating the caption.

Adding Gaussian noise to the input images.

Attention regularization is the most directly relevant technique. By penalizing the model when it fails to attend to relevant image regions, it encourages the model to focus on the important visual cues when generating the caption. This leads to more informative and faithful captions that are grounded in the image content. L1 regularization (A) promotes sparsity, Dropout (B) reduces overfitting, KL divergence regularization (C) encourages similarity to the prior distribution (which may not improve faithfulness), and adding Gaussian noise (E) improves robustness, but none of these directly address the issue of attention to relevant image regions.

NEW QUESTION 194
You are analyzing a dataset of customer reviews for a new product using Natural Language Processing (NLP). The dataset contains both positive and negative reviews, but a significant portion of the negative reviews uses sarcasm. Which of the following NLP techniques would be MOST effective in accurately identifying the sentiment expressed in sarcastic reviews?

Bag-of-words model with TF-IDF weighting.

Sentiment lexicon-based approach.

Fine-tuning a pre-trained transformer model (e.g., BERT, RoBERTa) on a dataset of sarcastic and non-sarcastic reviews.

Using a rule-based system that identifies keywords associated with positive or negative sentiment.

Calculating the average word embedding for each review.

Pre-trained transformer models capture contextual information and complex relationships between words, which is crucial for detecting sarcasm. Bag-of-words, sentiment lexicons, and rule-based systems rely on individual words or simple patterns and are easily fooled by sarcasm. Averaging word embeddings loses the contextual information vital for sarcasm detection.

NEW QUESTION 195
You are training a conditional GAN (cGAN) to generate images of animals based on text descriptions. Which of the following is the most crucial difference in the training process of a cGAN compared to a regular GAN?

The discriminator in a cGAN only receives real images as input.

The generator in a cGAN receives both random noise and the condition (text description) as input.

The cGAN uses a different loss function than a regular GAN.

The cGAN does not require a discriminator.

The discriminator in a cGAN does not receive generated images.

The key difference in a cGAN is that the generator takes an additional input, the condition (in this case, the text description), along with random noise. This allows the generator to learn a mapping from conditions to images, enabling the generation of images that match the given descriptions. The discriminator also receives the condition to evaluate if the generated image matches the corresponding condition. Other options are incorrect as Discriminator need both generated and real images with the conditioning variable to train correctly.

NEW QUESTION 196
You are evaluating two different generative A1 model architectures (Model A and Model B) for image generation. You use the Frechet Inception Distance (FID) score as your primary evaluation metric. Model A has a lower FID score than Model B. Which of the following statements are MOST accurate regarding the interpretation of the FID scores? (Select TWO)

Model A generates images that are more visually appealing to human observers.

Model A generates images that have a distribution more similar to the real image distribution used for calculating the FID score.

Model B generates images that are more diverse than Model A.

Model A is less likely to suffer from mode collapse than Model B.

Model B necessarily has better performance on downstream tasks using the generated images.

A lower FID score indicates that the generated images are statistically more similar to the real images (B). It also suggests that Model A is less prone to mode collapse (D), as it captures the data distribution better. FID score doesn’t guarantee visual appeal (A) or better performance on downstream tasks (E). Diversity (C) isn’t directly implied by a lower FID score alone.

NEW QUESTION 197
Which of the following evaluation metrics is MOST appropriate for assessing the performance of a multimodal generative A1 model that generates image captions based on images and audio descriptions?

Perplexity

BLEU (Bilingual Evaluation Understudy)

Inception Score

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

BLEU (B) is specifically designed to evaluate the quality of generated text by comparing it to reference captions. Perplexity is more suitable for language models, Inception Score for generated images, and MSE/RMSE for regression tasks.

NEW QUESTION 198
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.

Minibatch Discrimination helps the discriminator recognize and penalize the generator for producing similar outputs within a minibatch, thus encouraging diversity. Decreasing the discriminator’s learning rate or using a simpler generator might worsen the problem. Increasing batch size may help stabilize training but doesn’t directly address mode collapse. Reducing input noise would likely decrease diversity.

NEW QUESTION 199
You’re developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

Increase the learning rate for the audio modality pathway during training.

Apply modality-specific dropout to the image pathway.

Oversample the audio data during training.

Reduce the dimensionality of the image features before fusion.

Increase the batch size for each epoch.

Increasing the learning rate for the audio pathway allows it to update its weights more aggressively, potentially counteracting the image bias. Applying modality-specific dropout to the image pathway forces the model to rely less on image features and more on audio. Oversampling the audio data ensures that the model sees more examples from the audio modality during training. Reducing the dimensionality of the image features can prevent them from dominating the fusion process. Increasing batch size is not specific to each modality and does not directly deal with modality imbalance, but can influence training dynamics.

NEW QUESTION 200
You’re using NVIDIA Triton to serve a multimodal model: a CLIP text encoder and a StyleGAN image generator. You need to ensure high throughput and minimal latency. Which Triton backend configuration is most suitable for this scenario, assuming both models are optimized for NVIDIA GPUs?

A Python backend where both models are loaded into memory and inference is performed sequentially.

Two separate model repositories, one for CLIP (as ONNX) and one for StyleGAN (as TensorRT), served by two Triton instances on different GPUs.

A single model repository with two model instances (CLIP as ONNX, StyleGAN as TensorRT) served by a single Triton instance, leveraging concurrent execution.

A single model repository containing both models as TorchScript, served by a single Triton instance using the PyTorch backend.

Using just the Python backend with the models on CPU.

Option C is the most efficient. Serving both models within a single Triton instance and using optimized formats (ONNX and TensorRT) allows Triton to manage resources effectively and potentially overlap computation (concurrent execution) if the models allow for it, leading to higher throughput and lower latency. Using the python backend only is less efficient than the dedicated backends. Running on different GPUs increases cost unnecessarily. TorchScript might work but depends on the models.

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