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Die seit kurzem aktuellsten NVIDIA Generative AI LLMs Prüfungsunterlagen, 100% Garantie für Ihen Erfolg in der NVIDIA NCA-GENL Prüfungen!

Suchen Sie nach die geeignetsten Prüfungsunterlagen der NVIDIA NCA-GENL? Sorgen Sie noch um das Ordnen der Unterlagen? It-Pruefung als ein professioneller Lieferant der Software der IT-Zertifizierungsprüfung haben Ihnen die umfassendsten Unterlagen der NVIDIA NCA-GENL vorbereitet. Jetzt können Sie Zeit fürs Suchen gespart und direkt auf die NVIDIA NCA-GENL Prüfung vorbereiten!

NVIDIA NCA-GENL Prüfungsplan:

Thema
Einzelheiten

Thema 1

Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:

Thema 2

Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

Thema 3

Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

Thema 4

LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.

Thema 5

Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.

Thema 6

Experiment Design

Thema 7

This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.

Thema 8

Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

Thema 9

Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

Thema 10

Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.

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NVIDIA Generative AI LLMs NCA-GENL Prüfungsfragen mit Lösungen (Q24-Q29):

24. Frage
Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

A. Choosing another model architecture.
B. Increasing the model's parameter count.
C. Leveraging the system message.
D. Training the model with additional data.

Antwort: C

Begründung:
Prompt engineering involves designing inputs to guide large language models (LLMs) to produce desired outputs without modifying the model itself. Leveraging the system message is a key technique, where a predefined instruction or context is provided to the LLM to set the tone, role, or constraints for its responses.
NVIDIA's NeMo framework documentation on conversational AI highlights the use of system messages to improve the contextual accuracy of LLMs, especially in dialogue systems or task-specific applications. For instance, a system message like "You are a helpful technical assistant" ensures responses align with the intended role. Options A, B, and C involve model training or architectural changes, which are not part of prompt engineering.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

25. Frage
Which calculation is most commonly used to measure the semantic closeness of two text passages?

A. Euclidean distance
B. Hamming distance
C. Jaccard similarity
D. Cosine similarity

Antwort: D

Begründung:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

26. Frage
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

A. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
B. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.
C. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
D. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.

Antwort: D

Begründung:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA's documentation on generative AI (e.g., in the context of NVIDIA's work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central tomodels like DDPM (Denoising Diffusion Probabilistic Models). Option A is incorrect, as forward diffusion adds noise, not generates samples. Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks. Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
References:
NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."

27. Frage
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

A. It consists of removing a quantity of weights whose values are zero.
B. Helps reduce memory requirements and achieve better cache utilization.
C. It leads to a substantial loss of model accuracy.
D. It only involves reducing the number of bits of the parameters.
E. Quantization might help in saving power and reducing heat production.

Antwort: B,E

Begründung:
Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32- bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA's documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
* Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
References:
NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

28. Frage
Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

A. NeMo
B. NVIDIA Triton
C. HuggingFace
D. NVIDIA DeepStream

Antwort: B

Begründung:
NVIDIA Triton Inference Server is a high-performance framework designed for deploying machine learning models, including NLP models, in production environments. It supports optimized inference on GPUs, dynamic batching, and integration with frameworks like PyTorch and TensorFlow. According to NVIDIA's Triton documentation, it is ideal for deploying LLMs for real-time applications with low latency. Option A (DeepStream) is for video analytics, not NLP. Option B (HuggingFace) is a library for model development, not deployment. Option C (NeMo) is for training and fine-tuning, not production deployment.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

29. Frage
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Die NVIDIA NCA-GENL Zertifizierungsprüfung ist zur Zeit sehr beliebt bei den IT-Fachleuten. Durch die NVIDIA NCA-GENL Zertifizierungsprüfung werden Ihre Lebens-und Arbeitsverhältnisse verbessert. Daneben wird Ihre Position in der IT-Branche gefestigt.

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