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[General] Valid Test NCA-GENL Tutorial - NCA-GENL Practice Test Fee

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【General】 Valid Test NCA-GENL Tutorial - NCA-GENL Practice Test Fee

Posted at yesterday 17:24      View：5 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Free & New NCA-GENL dumps are available on Google Drive shared by DumpsValid: https://drive.google.com/open?id=1OBF8sGJJlW68Y7NVj6IbaHDeArF7h5dp
The second format is web-based NCA-GENL practice exam and can be accessed through browsers and candidates can take it online. The students don’t need to install or use any plugins or software to attempt the web-based practice exam. The third and last form is the NVIDIA NCA-GENL desktop practice test software that can be used from Windows computers. Candidates that have Windows laptops or computers can take the NCA-GENL practice exam efficiently.
NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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.
Topic 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.
Topic 5
  • 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 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.

Pass Guaranteed NVIDIA - NCA-GENL - NVIDIA Generative AI LLMs Pass-Sure Valid Test TutorialIt is similar to the NCA-GENL desktop-based software, with all the elements of the desktop practice exam. This mock exam can be accessed from any browser and does not require installation. The NVIDIA NCA-GENL questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based NVIDIA NCA-GENL Practice Test immediately through any operating system and browsers.
NVIDIA Generative AI LLMs Sample Questions (Q92-Q97):NEW QUESTION # 92
Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?
  • A. NVIDIA DeepStream
  • B. NVIDIA Triton
  • C. HuggingFace
  • D. NeMo
Answer: B
Explanation:
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

NEW QUESTION # 93
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)
  • A. Cheaper computational costs during inference.
  • B. It is easier to explain the predictions.
  • C. Single generic model can do more than one task.
  • D. Trained without the need for labeled data.
  • E. Smaller latency, higher throughput.
Answer: A,C
Explanation:
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

NEW QUESTION # 94
Which of the following is a key characteristic of Rapid Application Development (RAD)?
  • A. Minimal user feedback during the development process.
  • B. Extensive upfront planning before any development.
  • C. Iterative prototyping with active user involvement.
  • D. Linear progression through predefined project phases.
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 # 95
Which metric is commonly used to evaluate machine-translation models?
  • A. Perplexity
  • B. BLEU score
  • C. ROUGE score
  • D. F1 Score
Answer: B
Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."

NEW QUESTION # 96
"Hallucinations" is a term coined to describe when LLM models produce what?
  • A. Correct sounding results that are wrong.
  • B. Images from a prompt description.
  • C. Outputs are only similar to the input data.
  • D. Grammatically incorrect or broken outputs.
Answer: A
Explanation:
In the context of LLMs, "hallucinations" refer to outputs that sound plausible and correct but are factually incorrect or fabricated, as emphasized in NVIDIA's Generative AI and LLMs course. This occurs when models generate responses based on patterns in training data without grounding in factual knowledge, leading to misleading or invented information. Option A is incorrect, as hallucinations are not about similarity to input data but about factual inaccuracies. Option B is wrong, as hallucinations typically refer to text, not image generation. Option D is inaccurate, as hallucinations are grammatically coherent but factually wrong. The course states: "Hallucinations in LLMs occur when models produce correct-sounding but factually incorrect outputs, posing challenges for ensuring trustworthy AI." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 97
......
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