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[General] NCA-GENL Associate Level Exam & NCA-GENL Latest Exam Labs

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【General】 NCA-GENL Associate Level Exam & NCA-GENL Latest Exam Labs

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NVIDIA Generative AI LLMs Sample Questions (Q47-Q52):NEW QUESTION # 47
You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?
  • A. RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.
  • B. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.
  • C. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.
Answer: B
Explanation:
RAPIDS is a suite of open-source libraries designed to accelerate data science workflows by leveraging GPU processing, as emphasized in NVIDIA's Generative AI and LLMs course. It enables on-GPU processing of computationally expensive calculations, such as data preprocessing and machine learning tasks, using libraries like cuDF and cuML. Additionally, RAPIDS minimizes CPU-GPU memory transfers by performing operations directly on the GPU, reducing latency and improving performance. Options A and B are identical and correct, reflecting RAPIDS' core functionality. Option C is incorrect, as RAPIDS does not focus on PCIe bus throughput or "word-doubling," which is not a relevant concept. Option D is wrong, as RAPIDS does not rely on lossless compression for acceleration but on GPU-parallel processing. The course notes: "RAPIDS accelerates data science by enabling GPU-based processing of computationally intensive tasks and minimizing CPU-GPU memory transfers, significantly speeding up workflows." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 48
Why do we need positional encoding in transformer-based models?
  • A. To reduce the dimensionality of the input data.
  • B. To increase the throughput of the model.
  • C. To prevent overfitting of the model.
  • D. To represent the order of elements in a sequence.
Answer: D
Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html

NEW QUESTION # 49
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 allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
  • D. A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.
  • E. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.
Answer: A,C
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 # 50
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)
  • A. Ensuring that CPUs are running at full clock speed.
  • B. Subsampling datasets to provide rapid but approximate answers.
  • C. Enabling data processing to scale to multiple GPUs.
  • D. Using the GPU for parallel processing of data.
  • E. Providing more memory for data analysis.
Answer: C,D
Explanation:
RAPIDS is an open-source suite of GPU-accelerated data science libraries developed by NVIDIA to speed up data processing and machine learning workflows. According to NVIDIA's RAPIDS documentation, its key advantages include:
* Option C: Using GPUs for parallel processing, which significantly accelerates computations for tasks like data manipulation and machine learning compared to CPU-based processing.
References:
NVIDIA RAPIDS Documentation:https://rapids.ai/

NEW QUESTION # 51
Which of the following best describes Word2vec?
  • A. A database management system designed for storing and querying word data.
  • B. A deep learning algorithm used to generate word embeddings from text data.
  • C. A statistical technique used to analyze word frequency in a text corpus.
  • D. A programming language used to build artificial intelligence models.
Answer: B
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 # 52
......
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