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NVIDIA Generative AI Multimodal Sample Questions (Q170-Q175):

NEW QUESTION # 170
You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)

A. Decrease the learning rate.
B. Replace the LSTM with a simpler RNN in the question encoder.
C. Use a more sophisticated attention mechanism that attends to relevant image regions based on the question.
D. Increase the size of the image embedding.
E. Train the model on a larger dataset with more diverse and complex questions/answers.

Answer: C,E

Explanation:
More sophisticated attention mechanisms help the model focus on relevant image regions. A larger, more diverse dataset provides the model with more examples of complex reasoning scenarios. Increasing the image embedding size may help but is not as targeted. Decreasing the learning rate is a general optimization technique, and using a simpler RNN would likely degrade performance.

NEW QUESTION # 171
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?

A. The cGAN uses a different loss function than a regular GAN.
B. The cGAN does not require a discriminator.
C. The generator in a cGAN receives both random noise and the condition (text description) as input.
D. The discriminator in a cGAN does not receive generated images.
E. The discriminator in a cGAN only receives real images as input.

Answer: C

Explanation:
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 # 172
You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?

A. BLEU, ROUGE, and CIDEr.
B. Accuracy and Precision.
C. Fl-score and AUC.
D. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
E. Cosine Similarity and Euclidean Distance.

Answer: A

Explanation:
BLEU, ROUGE, and CIDEr are standard metrics used for evaluating the quality of generated text, particularly in image captioning and machine translation. These metrics compare the generated captions to reference captions and measure the similarity in terms of n-grams, word overlap, and other features. Other options are used for Classification problems (Accuracy Precision, Fl-score, AUC) and Regression Problems (MSE, RMSE).

NEW QUESTION # 173
You're designing a multimodal A1 system for autonomous driving that integrates data from cameras (images), LiDAR (point clouds), radar (time-series), and GPS (geospatial). The system needs to make real-time decisions in complex urban environments. Which hardware and software components are crucial for achieving low latency and high accuracy in data processing and fusion?

A. All of the above.
B. High-bandwidth, low-latency communication interfaces (e.g., PCle Gen4/5) for data transfer between sensors and processing units.
C. Real-time operating system (RTOS) for deterministic execution and minimal jitter.
D. Sensor fusion algorithms optimized for GPU acceleration.
E. NVIDIA GPUs with CUDA for accelerated processing of image and point cloud data.

Answer: A

Explanation:
All the options are critical for real-time performance. GPUs accelerate processing, high-bandwidth interfaces enable fast data transfer, RTOS ensures deterministic execution, and optimized sensor fusion algorithms maximize accuracy.

NEW QUESTION # 174
You are building a multimodal generative AI model to create personalized travel itineraries based on user preferences. The input data consists of text reviews of hotels, images of landmarks, audio clips of local music, and time-series data of weather patterns. Which of the following data curation techniques are MOST critical to ensure the quality and coherence of the final itinerary?

A. Sentiment analysis of text reviews to rank hotels based on positive feedback.
B. Prioritizing the most recent reviews, regardless of their content.
C. All of the above.
D. Temporal alignment of weather data with travel dates to suggest suitable activities.
E. Image captioning of landmarks to provide textual descriptions for the itinerary.

Answer: C

Explanation:
All the options are critical. Sentiment analysis helps rank hotels, image captioning provides descriptions, and temporal alignment ensures activities are weather-appropriate. Ignoring any of these aspects would negatively impact the quality of the personalized itinerary.

NEW QUESTION # 175
......

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100% Pass Quiz 2025 NVIDIA NCA-GENM: Reliable Study NVIDIA Generative AI Multimodal Group

NCA-GENM Vce Free | Reliable NCA-GENM Test Practice

NVIDIA Generative AI Multimodal Sample Questions (Q170-Q175):

Actual NVIDIA NCA-GENM Exam Dumps - Achieve Success In Exam

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