Blog - Digital Pushkraj Academy

Chris Walker Chris Walker

0 Course Enrolled • 0 Course Completed

Biography

最受推薦的NCA-GENL考題寶典，免費下載NCA-GENL學習資料得到妳想要的NVIDIA證書

P.S. KaoGuTi在Google Drive上分享了免費的2025 NVIDIA NCA-GENL考試題庫：https://drive.google.com/open?id=1Tx8B3s8M_qBjWoeoad4Dph_eSgWE0rHi

大家在準備考試的時候，可以結合本網站最新的 NVIDIA NCA-GENL 擬真試題去認真地做練習，這樣的話，可以為你的考試節省很多的時間。NVIDIA 的 NCA-GENL 考試整體來說還是不算複雜的，只要事先將擬真試題看好就沒有問題了。還有，做實驗題是要一定要多想想，這樣的話，才能將自身的一些素質提高上去。我們的考題網剛更新 NCA-GENL 題庫能確保考生能順利通過 NCA-GENL 考試，獲得 NVIDIA 認證證照。

NVIDIA NCA-GENL 考試大綱：

主題
簡介

主題 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.

主題 2

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.

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 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.

主題 5

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 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.

主題 7

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.

>> NCA-GENL考題寶典 <<

NVIDIA NCA-GENL熱門認證 & NCA-GENL考題資訊

言與行的距離到底有多遠？關鍵看人心，倘使心神明淨，意志堅強，則近在咫尺，垂手可及。我想你應該就是這樣的人吧。既然選擇了要通過NVIDIA的NCA-GENL認證考試，當然就得必須通過，KaoGuTi NVIDIA的NCA-GENL考試培訓資料是幫助通過考試的最佳選擇，也是表現你意志堅強的一種方式，KaoGuTi網站提供的培訓資料在互聯網上那是獨一無二的品質好，如果你想要通過NVIDIA的NCA-GENL考試認證，就購買KaoGuTi NVIDIA的NCA-GENL考試培訓資料。

最新的 NVIDIA-Certified Associate NCA-GENL 免費考試真題 (Q75-Q80):

問題 #75
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

A. It is easier to explain the predictions.
B. Trained without the need for labeled data.
C. Smaller latency, higher throughput.
D. Single generic model can do more than one task.
E. Cheaper computational costs during inference.

答案：D,E

解題說明：
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/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

問題 #76
Which of the following options describes best the NeMo Guardrails platform?

A. Developing and designing advanced machine learning models capable of interpreting and integrating various forms of data.
B. Building advanced data factories for generative AI services in the context of language models.
C. Ensuring the ethical use of artificial intelligence systems by monitoring and enforcing compliance with predefined rules and regulations.
D. Ensuring scalability and performance of large language models in pre-training and inference.

答案：C

解題說明：
The NVIDIA NeMo Guardrails platform is designed to ensure the ethical and safe use of AI systems, particularly LLMs, by enforcing predefined rules and regulations, as highlighted in NVIDIA's Generative AI and LLMs course. It provides a framework to monitor and control LLM outputs, preventing harmful or inappropriate responses and ensuring compliance with ethical guidelines. Option A is incorrect, as NeMo Guardrails focuses on safety, not scalability or performance. Option B is wrong, as it describes model development, not guardrails. Option D is inaccurate, as it does not pertain to data factories but to ethical AI enforcement. The course notes: "NeMo Guardrails ensures the ethical use of AI by monitoring and enforcing compliance with predefined rules, enhancing the safety and trustworthiness of LLM outputs." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA NeMo Framework User Guide.

問題 #77
Which of the following optimizations are provided by TensorRT? (Choose two.)

A. Variable learning rate
B. Residual connections
C. Multi-Stream Execution
D. Layer Fusion
E. Data augmentation

答案：C,D

解題說明：
NVIDIA TensorRT provides optimizations to enhance the performance of deep learning models during inference, as detailed in NVIDIA's Generative AI and LLMs course. Two key optimizations are multi-stream execution and layer fusion. Multi-stream execution allows parallel processing of multiple input streams on the GPU, improving throughput for concurrent inference tasks. Layer fusion combines multiple layers of a neural network (e.g., convolution and activation) into a single operation, reducing memory access and computation time. Option A, data augmentation, is incorrect, as it is a preprocessing technique, not a TensorRT optimization. Option B, variable learning rate, is a training technique, not relevant to inference. Option E, residual connections, is a model architecture feature, not a TensorRT optimization. The course states:
"TensorRT optimizes inference through techniques like layer fusion, which combines operations to reduce overhead, and multi-stream execution, which enables parallel processing for higher throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

問題 #78
In neural networks, the vanishing gradient problem refers to what problem or issue?

A. The issue of gradients becoming too large during backpropagation, leading to unstable training.
B. The problem of overfitting in neural networks, where the model performs well on the trainingdata but poorly on new, unseen data.
C. The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.
D. The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.

答案：C

解題說明：
The vanishing gradient problem occurs in deep neural networks when gradients become too small during backpropagation, causing slow convergence or stagnation in training, particularly in deeper layers. NVIDIA's documentation on deep learning fundamentals, such as in CUDA and cuDNN guides, explains that this issue is common in architectures like RNNs or deep feedforward networks with certain activation functions (e.g., sigmoid). Techniques like ReLU activation, batch normalization, or residual connections (used in transformers) mitigate this problem. Option A (overfitting) is unrelated to gradients. Option B describes the exploding gradient problem, not vanishing gradients. Option C (underfitting) is a performance issue, not a gradient-related problem.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

問題 #79
Which calculation is most commonly used to measure the semantic closeness of two text passages?

A. Jaccard similarity
B. Hamming distance
C. Cosine similarity
D. Euclidean distance

答案：C

解題說明：
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

問題 #80
......

你買了KaoGuTi的產品，我們會全力幫助你通過認證考試，而且還有免費的一年更新升級服務。如果官方改變了認證考試的大綱，我們會立即通知客戶。如果有我們的軟體有任何更新版本，都會立即推送給客戶。KaoGuTi是可以承諾幫你成功通過你的第一次NVIDIA NCA-GENL 認證考試。

NCA-GENL熱門認證: https://www.kaoguti.com/NCA-GENL_exam-pdf.html

P.S. KaoGuTi在Google Drive上分享了免費的2025 NVIDIA NCA-GENL考試題庫：https://drive.google.com/open?id=1Tx8B3s8M_qBjWoeoad4Dph_eSgWE0rHi

Chris Walker Chris Walker

Biography

Copyright © 2025 Digital Pushkraj Academy | All Rights Reserved.

Copyright © 2023 Digital Pushkraj Academy | All Rights Reserved.