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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques |
| Topic 2: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Topic 3: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 4: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 5: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
| Topic 6: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 7: Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
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NEW QUESTION # 28
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
Answer: B
Explanation:
Bias evaluation for facial recognition centers on measuring whether the model's accuracy - true positive rate, false positive rate, false match rate - is consistent across demographic subgroups (race, gender, age), rather than assuming a single aggregate accuracy figure represents performance fairly for all groups. This concern is well grounded empirically: independent benchmarking, including NIST's Face Recognition Vendor Test studies, has repeatedly documented substantial accuracy disparities across demographic groups in widely deployed facial recognition systems, with error rates for some subgroups measured many times higher than for others - a direct consequence of training data underrepresentation and the representativeness-bias issue covered elsewhere in this domain.
Processing speed (A) is a performance/latency engineering concern, not a bias or fairness concern - a model could process all demographic groups at identical speed while still exhibiting severe accuracy disparities between them. Facial expression recognition capability (C) addresses a different task dimension (emotion
/expression classification) than the identity-recognition bias question being asked. Operating system compatibility (D) is a software-deployment/engineering concern entirely unrelated to model fairness.
Subgroup accuracy disparities in facial recognition carry serious real-world consequences - misidentification risk in law enforcement or access-control contexts - which is why disaggregated (per-subgroup) evaluation, not just aggregate accuracy, is considered a baseline requirement under Trustworthy AI bias auditing practice.
Reference: Trustworthy AI domain - subgroup/disaggregated bias evaluation, fairness auditing.
NEW QUESTION # 29
You are working with a multimodal dataset containing medical images (X-rays) and corresponding patient reports (text). Some of the reports are missing or incomplete. Which of the following strategies would be most appropriate to handle this missing data in a multimodal AI model?
Answer: A
Explanation:
Using a multimodal autoencoder or a masked language model allows the model to leverage the relationship between the image and text modalities to infer the missing information. Discarding data or using simple imputation methods can lead to information loss or biased results. A multimodal autoencoder or masked language model can help to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.
NEW QUESTION # 30
Which framework is used for conversational AI models development?
Answer: D
Explanation:
NVIDIA NeMo is NVIDIA's open-source framework for building, training, and customizing conversational and generative AI models - spanning automatic speech recognition, natural language processing, text-to- speech, and large language models. It provides modular, reusable "neural modules" and pretrained checkpoints that developers fine-tune for domain-specific conversational applications (chatbots, voice assistants, transcription pipelines), and it integrates with NVIDIA's broader deployment stack (Triton, TensorRT) for production serving.
The distractors each target a different NVIDIA SDK's actual domain: NVIDIA Metropolis (A) is a platform for vision AI and intelligent video analytics (smart cities, retail analytics), not conversational AI. NVIDIA DeepStream (C) is a streaming analytics SDK for building GPU-accelerated video and audio processing pipelines, primarily targeting perception tasks rather than conversational model training. NVIDIA Clara (D) is a healthcare-specific application framework for medical imaging and genomics AI, unrelated to conversational AI development.
It's worth distinguishing NeMo from Riva: NeMo is the training/customization framework, while Riva is the corresponding deployment SDK optimized for low-latency, production speech and conversational AI inference. Exam questions sometimes probe this NeMo-versus-Riva distinction directly, so treat "build/train
/customize" as the NeMo signal and "deploy/production/low-latency" as the Riva signal.
Reference: Software Development and Engineering domain - NVIDIA NeMo framework; adjacent SDKs (Metropolis, DeepStream, Clara, Riva).
NEW QUESTION # 31
Which of the following techniques is most appropriate for mitigating the vanishing gradient problem in very deep neural networks, particularly when training generative models?
Answer: D
Explanation:
Residual connections (skip connections) allow gradients to flow more easily through the network by providing a direct path for the gradient to propagate, bypassing potential bottlenecks in the deeper layers. This is crucial for training very deep networks without the vanishing gradient problem hindering learning.
NEW QUESTION # 32
You have trained a multimodal model to generate descriptions for recipes, using images of the finished dish as one modality and a list of ingredients as another. When evaluating the generated descriptions, you notice the descriptions are factually correct in terms of ingredients but often fail to capture the stylistic nuances or tone of professionally written recipes. Which evaluation strategy would provide the most insightful feedback on this aspect of the model's performance?
Answer: B
Explanation:
Human evaluation is the most reliable way to assess subjective aspects of the generated text, such as stylistic appropriateness. While automated metrics like BLEU, ROUGE, and perplexity can provide insights into factual accuracy and fluency, they do not capture the nuances of style and tone as effectively as human judgment. Therefore, the correct answer is (D).
NEW QUESTION # 33
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