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[Hardware] High Pass-Rate NCA-GENL Certification Exam - Win Your NVIDIA Certificate with To

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【Hardware】 High Pass-Rate NCA-GENL Certification Exam - Win Your NVIDIA Certificate with To

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.
Topic 6
  • 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.
Topic 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.
Topic 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.:
Topic 9
  • 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.

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NVIDIA Generative AI LLMs Sample Questions (Q31-Q36):NEW QUESTION # 31
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. Paired t-test
  • B. Mann-Whitney U test
  • C. Chi-squared test
  • D. ANOVA test
Answer: A
Explanation:
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/deeplear ... /docs/en/stable/nlp
/model_finetuning.html

NEW QUESTION # 32
Which aspect in the development of ethical AI systems ensures they align with societal values and norms?
  • A. Achieving the highest possible level of prediction accuracy in AI models.
  • B. Developing AI systems with autonomy from human decision-making.
  • C. Implementing complex algorithms to enhance AI's problem-solving capabilities.
  • D. Ensuring AI systems have explicable decision-making processes.
Answer: D
Explanation:
Ensuring explicable decision-making processes, often referred to as explainability or interpretability, is critical for aligning AI systems with societal values and norms. NVIDIA's Trustworthy AI framework emphasizes that explainable AI allows stakeholders to understand how decisions are made, fostering trust and ensuring compliance with ethical standards. This is particularly important for addressing biases and ensuring fairness. Option A (prediction accuracy) is important but does not guarantee ethical alignment. Option B (complex algorithms) may improve performance but not societal alignment. Option C (autonomy) can conflict with ethical oversight, making it less desirable.
References:
NVIDIA Trustworthy AI:https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 33
In the context of language models, what does an autoregressive model predict?
  • A. The probability of the next token by looking at the previous and future input tokens.
  • B. The probability of the next token in a text given the previous tokens.
  • C. The next token solely using recurrent network or LSTM cells.
  • D. The probability of the next token using a Monte Carlo sampling of past tokens.
Answer: B
Explanation:
Autoregressive models are a cornerstone of modern language modeling, particularly in large language models (LLMs) like those discussed in NVIDIA's Generative AI and LLMs course. These models predict the probability of the next token in a sequence based solely on the preceding tokens, making them inherently sequential and unidirectional. This process is often referred to as "next-token prediction," where the model learns to generate text by estimating the conditional probability distribution of the next token given the context of all previous tokens. For example, given the sequence "The cat is," the model predicts the likelihood of the next word being "on," "in," or another token. This approach is fundamental to models like GPT, which rely on autoregressive decoding to generate coherent text. Unlike bidirectional models (e.g., BERT), which consider both previous and future tokens, autoregressive models focus only on past tokens, making option D incorrect. Options B and C are also inaccurate, as Monte Carlo sampling is not a standard method for next- token prediction in autoregressive models, and the prediction is not limited to recurrent networks or LSTM cells, as modern LLMs often use Transformer architectures. The course emphasizes this concept in the context of Transformer-based NLP: "Learn the basic concepts behind autoregressive generative models, including next-token prediction and its implementation within Transformer-based models." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 34
In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), which method is essential for understanding the contextual relationship between words in textual data?
  • A. Generating word clouds to visually represent word frequency and highlight key terms.
  • B. Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.
  • C. Applying sentiment analysis to gauge the overall sentiment expressed in a text.
  • D. Computing the frequency of individual words to identify the most common terms in a text.
Answer: B
Explanation:
In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), creating n-gram models is essential for understanding the contextual relationships between words, as highlighted in NVIDIA's Generative AI and LLMs course. N-grams (e.g., bigrams, trigrams) capture sequences of words, revealing patterns and dependencies in text, such as common phrases or syntactic structures, which are critical for NLU tasks like text generation or classification. Unlike single-word frequency analysis, n-grams provide insight into how words relate to each other in context. Option A is incorrect, as computing word frequencies focuses on individual terms, missing contextual relationships. Option B is wrong, as sentiment analysis targets overall text sentiment, not word relationships. Option C is inaccurate, as word clouds visualize frequency, not contextual patterns. The course notes: "N-gram models are used in EDA for NLU to analyze word sequence patterns, such as bigrams and trigrams, to understand contextual relationships in textual data." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 35
Which library is used to accelerate data preparation operations on the GPU?
  • A. cuGraph
  • B. cuDF
  • C. XGBoost
  • D. cuML
Answer: B
Explanation:
cuDF is a GPU-accelerated data manipulation library within the RAPIDS ecosystem, designed to speed up data preparation operations such as filtering, joining, and aggregating large datasets. As highlighted in NVIDIA's Generative AI and LLMs course, cuDF provides pandas-like functionality for data preprocessing but leverages GPU parallelism to achieve significant performance improvements, making it ideal for data science workflows involving large-scale data preparation. Option A, cuML, is incorrect, as it focuses on machine learning algorithms, not data preparation. Option B, XGBoost, is a gradient boosting framework, not a data preparation library. Option D, cuGraph, is used for graph analytics, not general data preparation. The course notes: "RAPIDS cuDF accelerates data preparation operations by enabling GPU-based processing, offering pandas-like functionality with significant speedups for tasks like data filtering and transformation." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 36
......
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