NCA-GENM Training Materials: NVIDIA Generative AI Multimodal & NCA-GENM Cram PDF & NCA-GENM Exam Guide

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NVIDIA Generative AI Multimodal Sample Questions (Q91-Q96):

NEW QUESTION # 91
You are tasked with visualizing the performance of a Generative A1 model across different categories of input dat a. You need to show both the accuracy and the number of data points in each category. Which visualization technique would be MOST effective for this purpose?

A. A table showing the accuracy and sample size for each category.
B. A combination chart (e.g., bar and line) with bars showing the accuracy and a line showing the sample size.
C. A bar chart showing the accuracy for each category, with error bars indicating the sample size.
D. A scatter plot showing the relationship between accuracy and sample size for each category.
E. A pie chart showing the accuracy for each category.

Answer: B

Explanation:
A combination chart effectively displays two different types of data (accuracy and sample size) for each category. It allows for easy comparison and identification of trends. A pie chart is not suitable for showing multiple data points. Error bars don't effectively represent sample size in a standard bar chart. A scatter plot is more appropriate for showing the relationship between two continuous variables. A table lacks the visual impact of a chart.

NEW QUESTION # 92
Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

Answer: E

Explanation:
The correct code uses ' AutoModelForSeq2SeqLM' because BLIP (used in the example) is a sequence-to-sequence model. The processor correctly handles the image and text, and 'model.generate' produces the answer which is then decoded. 'AutoModelForQuestionAnswering' is not a generic class and won't work correctly with BLIP without additional adaptation.

NEW QUESTION # 93
You are developing a multimodal model that processes text and time-series data to predict equipment failure. The text data consists of maintenance logs, and the time-series data consists of sensor readings. You observe that the model performs poorly when there are abrupt changes in sensor readings that are not reflected in the maintenance logs. How would you address this data integration challenge? (Select all that apply).

A. Downsample the time-series data to match the frequency of the text data.
B. Use a transformer-based model with cross-attention to allow the model to attend to relevant time-series features when processing the text data.
C. Implement a change-point detection algorithm to identify abrupt changes in sensor readings and generate synthetic text descriptions of these events to augment the maintenance logs.
D. Ignore the sensor readings and focus only on the maintenance logs.
E. All of the above

Answer: B,C

Explanation:
Implementing change-point detection and generating synthetic text descriptions helps bridge the gap between the two modalities. Cross-attention allows the model to dynamically integrate information from both modalities. Downsampling the time series data could lead to loss of information. Option E is incorrect because downsampling may hide crucial details in the time-series data.

NEW QUESTION # 94
You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?

A. Increasing the learning rate significantly
B. Freezing the weights of earlier layers in the network
C. Elastic Weight Consolidation (EWC)
D. Adding more data augmentation
E. Using a smaller batch size

Answer: B,C

NEW QUESTION # 95
You're tasked with building a generative A1 model for music composition. You have a large dataset of MIDl files, but the data is inconsistent in terms of tempo, key, and instrumentation. What are the crucial data transformation steps needed before training the model?

A. Standardizing the instrumentation by mapping different instrument patches to a predefined set.
B. Normalizing the tempo of all MIDl files to a standard BPM.
C. Converting all MIDl files to MP3 format.
D. Transposing all MIDl files to the same key (e.g., C major/A minor).
E. Rescaling the MIDl note velocities to a uniform range.

Answer: A,B,D,E

Explanation:
Consistent data is crucial for training a generative model. Rescaling note velocities ensures a uniform dynamic range. Transposing to a common key removes key-related variations. Normalizing tempo ensures consistent timing information. Standardizing instrumentation helps the model learn consistent relationships between notes and instruments. Converting to MP3 loses the symbolic representation of music notes which is vital for this application, so E is incorrect.

NEW QUESTION # 96
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NCA-GENM TRAINING MATERIALS: NVIDIA GENERATIVE AI MULTIMODAL & NCA-GENM CRAM PDF & NCA-GENM EXAM GUIDE

NCA-GENM Training Materials: NVIDIA Generative AI Multimodal & NCA-GENM Cram PDF & NCA-GENM Exam Guide