PDF Version DemoNVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Associate - Generative AI Multimodal |
| Exam Number: | NCA-GENM |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Exam Format: | Multiple-choice |
| Passing Score: | Not publicly disclosed |
| Exam Price: | $125 USD |
| Available Languages: | English |
| Related Certifications: | NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL) NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO) |
| Certificate Validity Period: | 2 years |
| Sample Questions: | NVIDIA NCA-GENM Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | A basic understanding of generative AI and multimodal models. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal/ |
NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 2: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 3: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 4: Experimentation | 25% | - Experimental design - Hypothesis testing - A/B testing - Model evaluation and comparison |
| Topic 5: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 6: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 7: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
Question #1
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. Pie chart
B. Scatter plot
C. Line chart
D. Bar chart
Question #2
In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.
A. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
B. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
C. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
D. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
E. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.
Question #3
Which of the following is a component of the Content Authenticity Initiative?
A. Ethical AI development
B. Content validity
C. Data encryption
D. Content credential
Question #4
Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?
A. To optimize search algorithms for faster data retrieval.
B. To produce new data that is similar to the training data.
C. To classify and categorize data based on patterns and features.
D. To optimize decision-making processes based on historical data.
Question #5
Which of the following tasks can be performed using the transformer LLM encoder model?
A. Image generation
B. Semantic analysis
C. Speech recognition
D. Generating code
Solutions:
| Question #1 Correct Answer: A | Question #2 Correct Answer: A,C | Question #3 Correct Answer: D | Question #4 Correct Answer: B | Question #5 Correct Answer: B |
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