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NVIDIA NCA-GENL Prüfungsplan:

Thema	Einzelheiten
Thema 1	Experiment Design
Thema 2	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 3	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 4	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 5	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 6	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 7	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 8	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.:

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Die NVIDIA NCA-GENL Zertifizierungsprüfung ist der erste Schritt zum Berufserfolg fur IT-Fachleute. Durch die NVIDIA NCA-GENL Zertifizierungsprüfung haben Sie schon den ersten Fuß auf die Spitze Ihrer Karriere gesetzt. Pass4Test wird Ihnen helfen, die NVIDIA NCA-GENL Zertifizierungsprüfung zu bestehen.

NVIDIA Generative AI LLMs NCA-GENL Prüfungsfragen mit Lösungen (Q27-Q32):

27. Frage
In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?

A. AI models on reinforcement learning tasks.
B. AI models on speech recognition tasks.
C. AI models on image recognition tasks.
D. AI models on a range of natural language understanding tasks.

Antwort: D

Begründung:
The General Language Understanding Evaluation (GLUE) benchmark is a widely used standard for evaluating AI models on a diverse set of natural language understanding (NLU) tasks, as covered in NVIDIA' s Generative AI and LLMs course. GLUE includes tasks like sentiment analysis, question answering, and textual entailment, designed to test a model's ability to understand and reason about language across multiple domains. It provides a standardized way to compare model performance on NLU. Option A is incorrect, as GLUE does not evaluate speech recognition. Option B is wrong, as it pertains to image recognition, unrelated to GLUE. Option D is inaccurate, as GLUE focuses on NLU, not reinforcement learning. The course states:
"The GLUE benchmark is used to evaluate AI models on a range of natural language understanding tasks, providing a comprehensive assessment of their language processing capabilities." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

28. Frage
How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

A. RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.
B. RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.
C. RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.
D. RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.

Antwort: D

Begründung:
Retrieval-Augmented Generation (RAG) enhances trustworthy AI by generating responses that cite reference material from an external knowledge base, ensuring transparency and verifiability, as discussed in NVIDIA's Generative AI and LLMs course. RAG combines a retriever to fetch relevant documents with a generator to produce responses, allowing outputs to be grounded in verifiable sources, reducing hallucinations and improving trust. Option A is incorrect, as RAG does not focus on security features like confidential computing. Option B is wrong, as RAG is unrelated to energy efficiency. Option C is inaccurate, as RAG does not align models but integrates retrieved knowledge. The course notes: "RAG enhances trustworthy AI by generating responses with citations from external knowledge bases, improving transparency and verifiability of outputs." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

29. Frage
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

A. Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.
B. Simplified API for classical machine learning algorithms like SVM.
C. Built-in support for CPU-based data preprocessing pipelines.
D. Automatic conversion of models to ONNX format for cross-platform deployment.

Antwort: A

Begründung:
The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA's TensorRT, enabling GPU-accelerated training and inference. NVIDIA's NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixed- precision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

30. Frage
Which tool would you use to select training data with specific keywords?

A. JSON parser
B. Regular expression filter
C. ActionScript
D. Tableau dashboard

Antwort: B

Begründung:
Regular expression (regex) filters are widely used in data preprocessing to select text data containing specific keywords or patterns. NVIDIA's documentation on data preprocessing for NLP tasks, such as in NeMo, highlights regex as a standard tool for filtering datasets based on textual criteria, enabling efficient data curation. For example, a regex pattern like .*keyword.* can select all texts containing "keyword." Option A (ActionScript) is a programming language for multimedia, not data filtering. Option B (Tableau) is for visualization, not text filtering. Option C (JSON parser) is for structured data, not keyword-based text selection.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

31. Frage
When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?

A. Mann-Whitney U test
B. ANOVA test
C. Chi-squared test
D. Paired t-test

Antwort: D

Begründung:
The paired t-test is the most appropriate statistical test to compare the performance (e.g., accuracy) of two large language models (LLMs) on the same question-answering dataset, assuming the data follows a normal distribution. This test evaluates whether the mean difference in paired observations (e.g., accuracy on each question) is statistically significant. NVIDIA's documentation on model evaluation in NeMo suggests using paired statistical tests for comparing model performance on identical datasets to account for correlated errors.
Option A (Chi-squared test) is for categorical data, not continuous metrics like accuracy. Option C (Mann- Whitney U test) is non-parametric and used for non-normal data. Option D (ANOVA) is for comparing more than two groups, not two models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html

32. Frage
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NCA-GENL zu bestehen mit allseitigen Garantien

NVIDIA NCA-GENL Prüfungsplan:

NCA-GENL Übungsmaterialien & NCA-GENL Deutsch Prüfungsfragen

NVIDIA Generative AI LLMs NCA-GENL Prüfungsfragen mit Lösungen (Q27-Q32):

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