[2025年更新]NCA-GENMのPDF問題完璧見込みでGoShiken練習試験合格させます
質問 # 165
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?
- A. Implementing a re-weighting scheme that gives higher weight to training examples containing the problematic objects.
- B. Applying a larger kernel size in the convolutional layers of the model.
- C. Adding more layers to the generative model's architecture.
- D. Reducing the learning rate during training.
- E. Increasing the batch size during training.
正解：A
解説：
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.
質問 # 166
Consider a scenario where you are developing a multimodal A1 system to translate sign language videos into text. The system utilizes a CNN for processing video frames and an RNN for generating the text sequence. During evaluation, you observe that the system struggles to accurately translate signs that involve complex hand movements or subtle facial expressions. What are the MOST effective strategies to improve performance in this specific scenario? (Select TWO)
- A. Incorporate a 3D CNN architecture to better capture spatial-temporal information from the video.
- B. Augment the training data with variations of the sign language videos, including different camera angles, lighting conditions, and signers.
- C. Replace the RNN with a simpler feed forward neural network for faster processing.
- D. Increase the depth of the CNN to capture more fine-grained visual features.
- E. Reduce the frame rate of the input video to decrease computational load.
正解：A、B
解説：
3D CNNs are designed to capture spatial-temporal information, which is essential for recognizing complex hand movements. Data augmentation helps the model generalize to different conditions and variations in signing style. Reducing frame rate (A) would likely worsen performance. Replacing the RNN with a feed forward network (D) would remove the ability to model sequential information, which is critical for translation. Increasing the depth of the CNN (B) might help, but a 3D CNN is more directly suited to the task.
質問 # 167
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?
- A. Chi-squared test
- B. Pearson correlation coefficient
- C. Cohen's Kappa
- D. Krippendorff's Alpha
- E. Spearman's rank correlation coefficient
正解：D
解説：
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.
質問 # 168
You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?
- A. Employing only IXI convolutions in the bottleneck of the U-Net architecture to reduce computational complexity.
- B. Using only strided convolutions for downsampling and transposed convolutions for upsampling without skip connections.
- C. Ensuring that the number of downsampling and upsampling blocks are equal, and employing skip connections from corresponding encoder layers to decoder layers.
- D. Using a batch size of 1 during training to simplify memory management.
- E. Using max pooling with a kernel size of 3x3 and stride of 2 for downsampling, and nearest neighbor interpolation for upsampling.
正解：C
解説：
Maintaining the same resolution in IJ-Net requires symmetric downsampling and upsampling and the crucial use of skip connections. These connections pass feature maps from the downsampling (encoder) path to the corresponding layers in the upsampling (decoder) path, allowing the decoder to recover spatial information lost during downsampling. Option A omits skip connections. Option C's nearest neighbor interpolation can lead to blocky artifacts. Option D describes a bottleneck optimization unrelated to output resolution. Option E is about training parameters.
質問 # 169
You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?
- A. Variations in background noise affecting audio quality.
- B. Differences in lighting conditions influencing facial expression recognition.
- C. Synchronization issues between audio and video streams.
- D. All of the above.
- E. Subjectivity in emotional expression across cultures and individuals.
正解：D
解説：
All the options pose challenges. Background noise affects audio analysis, varying lighting impairs facial recognition, subjective emotional expression necessitates robust models, and synchronization issues hinder accurate multimodal integration.
質問 # 170
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?
- A. Increase the temperature parameter during text generation.
- B. Use a smaller vccabulary size.
- C. Implement beam search with a larger beam width.
- D. Increase the number of attention heads in the Transformer.
- E. Decrease the learning rate of the model during training.
正解：A
解説：
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.
質問 # 171
Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?
- A. Decreasing the number of inference steps to speed up the generation process.
- B. Removing the word 'sitting' from the prompt to make it more general.
- C. Adding more unrelated keywords to the prompt to increase diversity.
- D. Increasing the 'guidance_scale' and adding a negative prompt such as 'blurry, distorted'.
- E. Using a smaller image resolution to reduce computational cost.
正解：D
解説：
Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.
質問 # 172
You have a large dataset of images and text descriptions. You want to train a model that can perform both image captioning (generating text from images) and text-to-image generation (generating images from text). What architectural approach is best suited for this multimodal bi-directional task?
- A. Use a shared encoder for both images and text, and separate decoders for generating text and images.
- B. Use a single transformer model with a shared vocabulary and treat both image and text as sequences of tokens.
- C. Use a generative adversarial network (GAN) for generating the outputs.
- D. Use separate encoders for images and text, a shared attention mechanism, and separate decoders for generating text and images.
- E. Train two separate models: one for image captioning and one for text-to-image generation.
正解：D
解説：
Separate encoders for images and text allow for specialized feature extraction for each modality. A shared attention mechanism enables cross-modal interaction, allowing the model to attend to relevant parts of both the image and text representations. Separate decoders allow for generating outputs in different modalities. Training separate models is less efficient and doesn't leverage shared knowledge. A shared encoder might struggle to capture modality-specific features effectively. A single transformer might be computationally expensive and difficult to train. GAN is suitable for image generation, not really bidirectional tasks.
質問 # 173
You're training a multimodal model for image and text retrieval. Given an image, the model should retrieve the most relevant text description from a database, and vice-vers a. You're using a dual-encoder architecture, where one encoder processes images and the other processes text, projecting them into a shared embedding space. What is the most effective way to train the model to ensure that semantically similar images and texts have close embeddings, while dissimilar ones have distant embeddings?
- A. Use a reconstruction loss that forces the model to reconstruct the input image from its text embedding and vice-versa.
- B. Train the encoders independently using separate supervised tasks for image and text classification.
- C. Use a simple L1 loss between the image and text embeddings-
- D. Apply adversarial training to make the embeddings indistinguishable between the two modalities.
- E. Use a contrastive loss function that minimizes the distance between embeddings of matching image-text pairs and maximizes the distance between embeddings of non-matching pairs. Example: Triplet Loss, InfoNCE.
正解：E
解説：
Contrastive loss functions are specifically designed for learning embeddings where similarity is defined by distance. They directly encourage similar items to be close and dissimilar items to be far apart. Independent training doesn't enforce the multimodal relationship. Reconstruction loss focuses on regenerating the input, not similarity. Adversarial training aims for indistinguishability, not meaningful embeddings. L1 Loss is a basic distance metric but less effective than contrastive losses for learning semantic similarity
質問 # 174
You are working with a multimodal generative model that combines text and image inputs. The model's performance is suboptimal when generating images conditioned on complex text descriptions. Which data analysis technique would be MOST effective in identifying the root cause of this issue?
- A. Analyzing the correlation between the complexity of the text descriptions (e.g., number of words, sentence structure) and the quality of the generated images.
- B. Calculating the mean pixel intensity of the images.
- C. Performing a sentiment analysis of the text descriptions to identify potential biases.
- D. Measuring the frequency of different objects appearing in the images.
- E. Calculating the average image resolution in the dataset.
正解：A
解説：
Analyzing the correlation between text complexity and image quality directly investigates whether the model struggles with more intricate text descriptions. Sentiment analysis is relevant for identifying biases, but doesn't address the complexity issue directly. The average image resolution and pixel intensity are general image statistics and not specific to the multimodal problem. Object frequency in images can be useful but less direct than correlating text complexity with image quality.
質問 # 175
Which of the following are key challenges specific to training multimodal models compared to unimodal models? (Select TWO)
- A. The relative simplicity of unimodal model architectures.
- B. Aligning and fusing information from different modalities with potentially different representations and noise characteristics.
- C. The difficulty of evaluating the performance of multimodal models.
- D. Increased computational cost due to processing multiple data types.
- E. The lack of readily available pre-trained models for different modalities.
正解：B、D
解説：
Multimodal models inherently require more computation due to the need to process multiple data types. The core challenge lies in effectively aligning and fusing the information from these different modalities, especially given the potential for different representations and noise levels. While pre-trained models may exist, the alignment problem remains. Evaluation and model simplicity aren't modality specific challenges.
質問 # 176
Given the following code snippet using NVIDIA Triton Inference Server for deploying a multimodal model:
What does 'format: FORMAT NCHW' signify for the 'image_input'?
- A. The image data is in a channel-first format (Number of Images, Channels, Height, Width).
- B. The image data is normalized to a range between 0 and 1.
- C. The image data is in a channel-last format (Number of Images, Height, Width, Channels).
- D. The image data is represented as a NumPy array.
- E. The image data is in a compressed JPEG format.
正解：A
解説：
'FORMAT NCHW' specifies that the image data is organized in a channel-first format, where the dimensions represent (Number of Images, Channels, Height, Width). This is a common format used in deep learning frameworks like PyTorch. Other options are incorrect because they relate to different aspects of image data representation or format.
質問 # 177
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)
- A. Disable any logging or monitoring to reduce overhead.
- B. Deploy the model on a single CPU core to minimize resource contention.
- C. Utilize asynchronous processing and caching strategies to pre-compute and store frequently accessed data.
- D. Employ model quantization and pruning techniques to reduce model size and computational requirements.
- E. Prioritize model accuracy over inference speed.
正解：C、D
解説：
Model quantization and pruning reduce the model's size and computational complexity, leading to faster inference. Asynchronous processing and caching allow for pre-computation and storage of frequently used data, minimizing delays. Prioritizing accuracy over speed (A) is not suitable for real-time applications where responsiveness is crucial. Deploying on a single CPU core (D) would severely limit performance. Disabling logging (E) is detrimental for debugging and monitoring.
質問 # 178
A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?
- A. Immediately stop the car until the LiDAR sensor is fixed.
- B. Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
- C. Switch to a pre-programmed route that does not require LiDAR data.
- D. Rely solely on the camera images and ignore the missing LiDAR data.
- E. Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.
正解：B、E
解説：
Stopping the car or relying solely on a single modality is not a robust solution. Using sensor fusion to prioritize available modalities and estimate missing data allows the system to continue navigating safely. A Kalman filter is a specific technique for estimating the state of a system (in this case, the LiDAR point cloud) based on noisy sensor readings and a motion model.
質問 # 179
You have developed a multimodal model that predicts stock prices using news articles (text), historical stock data (time-series), and company financial reports (tabular data). You want to deploy this model using NVIDIA Triton Inference Server. Assume you have preprocessed the data and have individual models for each modality. What is the recommended approach to configure Triton for efficient and scalable multimodal inference?
- A. Deploy each modality-specific model as a separate Triton model and handle the fusion logic in the client application.
- B. Deploy the text model using ONNX Runtime, the time-series model using TensorFlow, and the tabular data model using PyTorch, and handle fusion manually.
- C. Deploy each modality-specific model as a separate Triton model and use a load balancer to distribute requests across the models.
- D. Create a single Triton model that encapsulates the entire multimodal pipeline, including preprocessing, individual modality models, and fusion logic, using the Ensemble Modeling feature.
- E. Convert all models to TensorRT for maximum inference speed, even if it compromises accuracy due to quantization.
正解：D
解説：
Using Triton's Ensemble Modeling feature (B) is the most efficient approach. It allows you to define a pipeline that includes preprocessing, individual modality models, and fusion logic within a single Triton model, simplifying deployment and management. This approach optimizes inter-model communication and reduces client-side overhead.
質問 # 180
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?
- A. Training the model on a larger dataset of music.
- B. Adding more layers to the model.
- C. Increasing the size of the model's hidden layers.
- D. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.
- E. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
正解：D、E
解説：
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.
質問 # 181
Explainable A1 (XAI) is crucial when deploying multimodal models, especially in high-stakes scenarios. Which technique is MOST appropriate for understanding the relative importance of different modalities (e.g., image vs. text) in a multimodal classification task?
- A. Visualizing the attention weights in the image processing component.
- B. Randomly shuffling the pixels in the input images and observing the change in model performance.
- C. Calculating the gradient of the output with respect to the input text embeddings.
- D. Ablation studies, where each modality is individually removed during inference and the change in model performance is measured.
- E. Performing a principal component analysis (PCA) on the combined feature vectors.
正解：D
解説：
Ablation studies provide a direct measure of the importance of each modality by observing how the model's performance changes when that modality is removed. This allows you to quantify the contribution of each modality to the overall prediction.
質問 # 182
In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?
- A. Data alignment is only necessary when dealing with time-series data.
- B. Data alignment is not relevant when using deep learning models.
- C. Data alignment ensures that data from different modalities refers to the same event or entity.
- D. Perfect data alignment is always achievable with proper preprocessing techniques.
- E. Misalignment can lead to spurious correlations and reduced model performance.
正解：C、E
解説：
Data alignment is crucial for ensuring that information from different modalities is correctly associated with the same event or entity. Misalignment can lead to incorrect relationships being learned by the model, resulting in poor performance. Data alignment is necessary for various types of multimodal data, not just time-series data. Perfect alignment is often difficult to achieve due to inherent noise and limitations in data collection. Deep learning models are also susceptible to issues caused by data misalignment.
質問 # 183
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