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| Section | Objectives |
|---|---|
| Generative AI Concepts | - Generative models
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Multimodal AI Systems | - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
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NEW QUESTION # 17
You are tasked with building a system that can generate captions for images. You want to use a transformer-based model. During inference, you notice that the model tends to generate repetitive captions. Which of the following decoding strategies could you use to mitigate this issue?
Answer: A,C
Explanation:
Greedy decoding often leads to repetitive sequences because it always chooses the most likely next word. Beam search, especially with a high beam width, can also lead to repetition if the highest-probability sequences contain repeated phrases. Beam search with a length penalty encourages the model to generate longer and more diverse captions. Random sampling and Top-k sampling introduce randomness into the decoding process, which can help to break out of repetitive loops and create more varied captions.
NEW QUESTION # 18
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'?
Answer: C
Explanation:
'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.
NEW QUESTION # 19
You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?
Answer: C
Explanation:
Exporting the NeMo model to ONNX (Open Neural Network Exchange) is essential for compatibility with Triton Inference Server and TensorRT optimization. ONNX provides a standard format that TensorRT can ingest and optimize for efficient inference on NVIDIA GPUs.
NEW QUESTION # 20
Which framework is used for conversational AI models development?
Answer: A
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 # 21
You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)
Answer: A,C
Explanation:
Cross-modal attention (B) allows the model to explicitly learn relationships between visual and musical features. End-to-end fine- tuning with a suitable loss function (D) encourages the model to learn a coherent mapping between the two modalities. Training the encoders and decoders separately won't necessarily improve coherence. Generating from random noise defeats the purpose. Limiting video length may reduce complexity but doesn't fundamentally address the alignment problem.
NEW QUESTION # 22
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