NVIDIA Generative AI Multimodal NCA-GENM exam torrent materials
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 2: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 3: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 4: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 5: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 6: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 7: Experimentation | 25% | - Model evaluation and comparison - Experimental design - A/B testing - Hypothesis testing |
NVIDIA Generative AI Multimodal Sample Questions:
1. What is the purpose of the cuDNN library?
A) To generate images from English text-prompts using CLIP.
B) To measure GPU usage and other metrics with Prometheus.
C) To implement GPU-accelerated data preparation and feature extraction.
D) To optimize deep neural network computations on NVIDIA GPUs.
2. During the process of data cleansing, which of the following steps is NOT typically performed?
A) Collecting additional data
B) Removing duplicates
C) Transforming data into a different format
D) Identifying and handling missing values
3. You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
A) Calculating the loss function of the model on the training set.
B) Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
C) Interviewing the developers of the AI model to assess its performance.
D) Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.
4. You are working with a large dataset and want to visualize the distribution of a continuous variable. Which type of data visualization would be most appropriate?
A) Line chart
B) Histogram chart
C) Pie chart
D) Bar chart
5. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A) Line chart
B) Scatter plot
C) Pie chart
D) Bar chart
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
