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[General] Quiz NVIDIA - Unparalleled NCA-GENL - NVIDIA Generative AI LLMs Valid Exam Patte

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【General】 Quiz NVIDIA - Unparalleled NCA-GENL - NVIDIA Generative AI LLMs Valid Exam Patte

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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 6
  • 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.
Topic 7
  • 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 8
  • 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 9
  • Experiment Design
Topic 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.

NVIDIA Generative AI LLMs Sample Questions (Q74-Q79):NEW QUESTION # 74
When should one use data clustering and visualization techniques such as tSNE or UMAP?
  • A. When there is a need to perform regression analysis and predict continuous numerical values.
  • B. When there is a need to reduce the dimensionality of the data and visualize the clusters in a lower- dimensional space.
  • C. When there is a need to perform feature extraction and identify important variables in the dataset.
  • D. When there is a need to handle missing values and impute them in the dataset.
Answer: B
Explanation:
Data clustering and visualization techniques like t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) are used to reduce the dimensionality of high- dimensional datasets and visualize clusters in a lower-dimensional space, typically 2D or 30 for interpretation.
As covered in NVIDIA's Generative AI and LLMs course, these techniques are particularly valuable in exploratory data analysis (EDA) for identifying patterns, groupings, or structure in data, such as clustering similar text embeddings in NLP tasks. They help reveal underlying relationships in complex datasets without requiring labeled data. Option A is incorrect, as t-SNE and UMAP are not designed for handling missing values, which is addressed by imputation techniques. Option B is wrong, as these methods are not used for regression analysis but for unsupervised visualization. Option D is inaccurate, as feature extraction is typically handled by methods like PCA or autoencoders, not t-SNE or UMAP, which focus on visualization. The course notes: "Techniques like t-SNE and UMAP are used to reduce data dimensionality and visualize clusters in lower-dimensional spaces, aiding in the understanding of data structure in NLP and other tasks." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 75
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. Chi-squared test
  • C. Mann-Whitney U 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 # 76
How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)
  • A. A/B testing helps validate the impact of changes or updates to deep learning models bystatistically analyzing the outcomes of different versions to make informed decisions for model optimization.
  • B. A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.
  • C. A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.
  • D. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.
  • E. A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
Answer: A,E
Explanation:
A/B testing is a controlled experimentation technique used to compare two versions of a system to determine which performs better. In the context of deep learning, NVIDIA's documentation on model optimization and deployment (e.g., Triton Inference Server) highlights its use in evaluating model performance:
* Option A: A/B testing validates changes (e.g., model updates or new features) by statistically comparing outcomes (e.g., accuracy or user engagement), enabling data-driven optimization decisions.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplear ... ide/docs/index.html

NEW QUESTION # 77
Which of the following best describes Word2vec?
  • A. A deep learning algorithm used to generate word embeddings from text data.
  • B. A statistical technique used to analyze word frequency in a text corpus.
  • C. A programming language used to build artificial intelligence models.
  • D. A database management system designed for storing and querying word data.
Answer: A
Explanation:
Word2Vec is a groundbreaking deep learning algorithm developed to create dense vector representations, or embeddings, of words based on their contextual usage in large text corpora. Unlike traditional methods like bag-of-words or TF-IDF, which rely on frequency counts and often result in sparse vectors, Word2Vec employs neural networks to learn continuous vector spaces where semantically similar words are positioned closer together. This enables machines to capture nuances such as synonyms, analogies, and relationships (e.
g., "king" - "man" + "woman" # "queen"). The algorithm operates through two primary architectures:
Continuous Bag-of-Words (CBOW), which predicts a target word from its surrounding context, and Skip- Gram, which does the reverse by predicting context words from a target word. Skip-Gram is particularly effective for rare words and larger datasets, while CBOW is faster and better for frequent words. In the context of NVIDIA's Generative AI and LLMs course, Word2Vec is highlighted as a foundational step in the evolution of text embeddings in natural language processing (NLP) tasks, paving the way for more advanced models like RNN-based embeddings and Transformers. This is essential for understanding how LLMs build upon these embeddings for tasks such as semantic analysis and language generation. Exact extract from the course description: "Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers." This positions Word2Vec as a key deep learning technique for generating meaningful word vectors from text data, distinguishing it from mere statistical frequency analysis or unrelated tools like programming languages or databases

NEW QUESTION # 78
Which of the following is a key characteristic of Rapid Application Development (RAD)?
  • A. Linear progression through predefined project phases.
  • B. Extensive upfront planning before any development.
  • C. Iterative prototyping with active user involvement.
  • D. Minimal user feedback during the development process.
Answer: C
Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs.
NVIDIA's documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models. Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility. Option C describes a linear waterfall approach, not RAD. Option D is false, as RAD relies heavily on user feedback.
References:
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

NEW QUESTION # 79
......
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