Achieve The Utmost Performance In NCA-GENL Exam Pass Guaranteed [Q12-Q26]

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Achieve The Utmost Performance In NCA-GENL Exam Pass Guaranteed

Achive your Success with Latest NVIDIA NCA-GENL Exam

NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:
Topic 3	Experiment Design
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	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.

NO.12 What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.

BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.

BLEU scores evaluate the ‘precision’ of translations, while ROUGE scores focus on the ‘recall’ of summarized text.

BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.

NO.13 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?

Cross-validation

Randomized controlled trial

Average entropy approximation

Greedy decoding

NO.14 In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

Multi-head attention reduces the model’s memory footprint by sharing weights across heads.

Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.

Multi-head attention eliminates the need for positional encodings in the input sequence.

Multi-head attention simplifies the training process by reducing the number of parameters.

NO.15 Which of the following optimizations are provided by TensorRT? (Choose two.)

Data augmentation

Variable learning rate

Multi-Stream Execution

Layer Fusion

Residual connections

NO.16 Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

Ensuring that CPUs are running at full clock speed.

Subsampling datasets to provide rapid but approximate answers.

Using the GPU for parallel processing of data.

Enabling data processing to scale to multiple GPUs.

Providing more memory for data analysis.

NO.17 Which of the following claims is correct about TensorRT and ONNX?

TensorRT is used for model deployment and ONNX is used for model interchange.

TensorRT is used for model deployment and ONNX is used for model creation.

TensorRT is used for model creation and ONNX is used for model interchange.

TensorRT is used for model creation and ONNX is used for model deployment.

NO.18 What is Retrieval Augmented Generation (RAG)?

RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.

RAG is a methodology that combines an information retrieval component with a response generator.

RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

RAG is a technique used to fine-tune pre-trained LLMs for improved performance.

NO.19 In the context of language models, what does an autoregressive model predict?

The probability of the next token by looking at the previous and future input tokens.

The probability of the next token in a text given the previous tokens.

The next token solely using recurrent network or LSTM cells.

The probability of the next token using a Monte Carlo sampling of past tokens.

NO.20 Which of the following is a parameter-efficient fine-tuning approach that one can use to fine-tune LLMs in a memory-efficient fashion?

TensorRT

NeMo

Chinchilla

LoRA

NO.21 In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

Decision tree

Support vector machine

Generative adversarial network

K-means clustering

NO.22 What is a foundation model in the context of Large Language Models (LLMs)?

A model that sets the state-of-the-art results for any of the tasks that compose the General Language Understanding Evaluation (GLUE) benchmark.

Any model trained on vast quantities of data at scale whose goal is to serve as a starter that can be adapted to a variety of downstream tasks.

Any model validated by the artificial intelligence safety institute as the foundation for building transformer-based applications.

Any model based on the foundation paper “Attention is all you need,” that uses recurrent neural networks and convolution layers.

NO.23 Which Python library is specifically designed for working with large language models (LLMs)?

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

NO.24 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/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.

A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.

A/B testing ensures that the deep learning model is robust and can handle different variations of input data.

A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.

NO.25 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?

Single hold-out validation with a fixed test set.

Stratified k-fold cross-validation.

Bootstrapping with random sampling.

Grid search for hyperparameter tuning.

NO.26 Why might stemming or lemmatizing text be considered a beneficial preprocessing step in the context of computing TF-IDF vectors for a corpus?

It reduces the number of unique tokens by collapsing variant forms of a word into their root form, potentially decreasing noise in the data.

It enhances the aesthetic appeal of the text, making it easier for readers to understand the document’s content.

It increases the complexity of the dataset by introducing more unique tokens, enhancing the distinctiveness of each document.

It guarantees an increase in the accuracy of TF-IDF vectors by ensuring more precise word usage distinction.