NCA-GENL높은통과율공부문제 - NCA-GENL퍼펙트덤프최신샘플

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>> NCA-GENL높은 통과율 공부문제 <<

NCA-GENL퍼펙트 덤프 최신 샘플, NCA-GENL참고자료

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최신 NVIDIA-Certified Associate NCA-GENL 무료샘플문제 (Q17-Q22):

질문 # 17
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 allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
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 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.

정답：A,E

설명：
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/deeplearning/triton-inference-server/user-guide/docs/index.html

질문 # 18
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Early stopping
B. Dropout
C. Random initialization
D. Transfer learning

정답：D

설명：
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

질문 # 19
You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?

A. A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.
B. A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.
C. A/B testing ensures that the deep learning model is robust and can handle different variations of input data.
D. A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.

정답：D

설명：
A/B testing is a controlled experimentation method used to compare two versions of a system (e.g., two model variants) to determine which performs better based on a predefined metric (e.g., user engagement, accuracy).
NVIDIA's documentation on model optimization and deployment, such as with Triton Inference Server, highlights A/B testing as a method to validate model improvements in real-world settings by comparing performance metrics statistically. For a recommendation system, A/B testing might compare click-through rates between two models. Option B is incorrect, as A/B testing focuses on outcomes, not designer commentary. Option C is misleading, as robustness is tested via other methods (e.g., stress testing). Option D is partially true but narrow, as A/B testing evaluates broader performance metrics, not just latency.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

질문 # 20
In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

A. Removing all non-Latin characters to simplify the input.
B. Applying Unicode normalization to standardize character encodings.
C. Converting text to phonetic representations for cross-lingual alignment.
D. Normalizing all text to a single script using transliteration.

정답：B

설명：
When preparing a multilingual dataset for fine-tuning an LLM, applying Unicode normalization (e.g., NFKC or NFC forms) is the most effective preprocessing technique to handle text from diverse scripts like Latin, Cyrillic, or Devanagari. Unicode normalization standardizes character encodings, ensuring that visually identical characters (e.g., precomposed vs. decomposed forms) are represented consistently, which improves model performance across languages. NVIDIA's NeMo documentation on multilingual NLP preprocessing recommends Unicode normalization to address encoding inconsistencies in diverse datasets. Option A (transliteration) may lose linguistic nuances. Option C (removing non-Latin characters) discards critical information. Option D (phonetic conversion) is impractical for text-based LLMs.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

질문 # 21
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

A. Subsampling datasets to provide rapid but approximate answers.
B. Providing more memory for data analysis.
C. Enabling data processing to scale to multiple GPUs.
D. Ensuring that CPUs are running at full clock speed.
E. Using the GPU for parallel processing of data.

정답：C,E

설명：
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/

질문 # 22
......

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NCA-GENL높은 통과율 공부문제 시험준비에 가장 좋은 기출문제 모음 자료

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