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新版NCA-GENL題庫 & NCA-GENL學習資料

你想参加NVIDIA的NCA-GENL认证考试吗？你身边肯定有很多人参加过这个考试了吧？因为这是一个很重要的考试，如果取得这个考试的认证资格，你将可以得到很多的好处。那麼，你想別人請教怎樣通過考試的方法了嗎？準備考試的方法有很多種，但是最高效的方法是用一個好的工具。那麼對你來說什麼才是好的工具呢？當然是Fast2test的NCA-GENL考古題了。

NVIDIA NCA-GENL 考試大綱：

主題
簡介

主題 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.

主題 2

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.

主題 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.

主題 4

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.

主題 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.

>> 新版NCA-GENL題庫 <<

NCA-GENL學習資料，NCA-GENL考題免費下載

當你進入Fast2test網站，你看到每天進入Fast2test網站的人那麼多，不禁感到意外。其實這很正常的，我們Fast2test網站每天給不同的考生提供培訓資料數不勝數，他們都是利用了我們的培訓資料才順利通過考試的，說明我們的NVIDIA的NCA-GENL考試認證培訓資料真起到了作用，如果你也想購買，那就不要錯過我們Fast2test網站，你一定會非常滿意的。

最新的 NVIDIA-Certified Associate NCA-GENL 免費考試真題 (Q34-Q39):

問題 #34
You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?

A. Greedy decoding
B. Cross-validation
C. Randomized controlled trial
D. Average entropy approximation

答案：B

解題說明：
When test data is unavailable, cross-validation is the most effective method to assess an AI model's performance using only the training dataset. Cross-validation involves splitting the training data into multiple subsets (folds), training the model on some folds, and validating it on others, repeatingthis process to estimate generalization performance. NVIDIA's documentation on machine learning workflows, particularly in the NeMo framework for model evaluation, highlights k-fold cross-validation as a standard technique for robust performance assessment when a separate test set is not available. Option B (randomized controlled trial) is a clinical or experimental method, not typically used for model evaluation. Option C (average entropy approximation) is not a standard evaluation method. Option D (greedy decoding) is a generation strategy for LLMs, not an evaluation technique.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

問題 #35
What is 'chunking' in Retrieval-Augmented Generation (RAG)?

A. Rewrite blocks of text to fill a context window.
B. A concept in RAG that refers to the training of large language models.
C. A method used in RAG to generate random text.
D. A technique used in RAG to split text into meaningful segments.

答案：D

解題說明：
Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM.
According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."

問題 #36
You are in need of customizing your LLM via prompt engineering, prompt learning, or parameter-efficient fine-tuning. Which framework helps you with all of these?

A. NVIDIA TensorRT
B. NVIDIA NeMo
C. NVIDIA DALI
D. NVIDIA Triton

答案：B

解題說明：
The NVIDIA NeMo framework is designed to support the development and customization of large language models (LLMs), including techniques like prompt engineering, prompt learning (e.g., prompt tuning), and parameter-efficient fine-tuning (e.g., LoRA), as emphasized in NVIDIA's Generative AI and LLMs course.
NeMo provides modular tools and pre-trained models that facilitate these customization methods, allowing users to adapt LLMs for specific tasks efficiently. Option A, TensorRT, is incorrect, as it focuses on inference optimization, not model customization. Option B, DALI, is a data loading library for computer vision, not LLMs. Option C, Triton, is an inference server, not a framework for LLM customization. The course notes:
"NVIDIA NeMo supports LLM customization through prompt engineering, prompt learning, and parameter- efficient fine-tuning, enabling flexible adaptation for NLP tasks." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA NeMo Framework User Guide.

問題 #37
In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

A. Stratified k-fold cross-validation.
B. Bootstrapping with random sampling.
C. Grid search for hyperparameter tuning.
D. Single hold-out validation with a fixed test set.

答案：A

解題說明：
Stratified k-fold cross-validation is a robust experimental design technique for evaluating machine learning models, especially on imbalanced datasets. It divides the dataset into k folds while preserving the class distribution in each fold, ensuring that the model is evaluated on representative samples of all classes.
NVIDIA's NeMo documentation on model evaluation recommends stratified cross-validation for tasks like text classification to obtain reliable performance estimates, particularly when classes are unevenly distributed (e.g., in sentiment analysis with few negative samples). Option A (single hold-out) is less robust, as it may not capture class imbalance. Option C (bootstrapping) introduces variability and is less suitable for imbalanced data. Option D (grid search) is for hyperparameter tuning, not performance estimation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/model_finetuning.html

問題 #38
In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?

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

答案：D

解題說明：
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.

問題 #39
......

NVIDIA NCA-GENL認證考試在競爭激烈的IT行業中越來越受歡迎，報名參加考試的人越來越多。但是它的難度並沒有減小，依然很難通過考試，畢竟這是個權威的檢驗電腦專業知識和資訊技術能力的考試。一般人為了通過NVIDIA NCA-GENL 認證考試都需要花費大量的時間和精力來復習備考。

NCA-GENL學習資料: https://tw.fast2test.com/NCA-GENL-premium-file.html

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