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Guide for NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL)

Quiz NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL)

Updated on 2024/12/29

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Guide for NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL)

Updated on 2024/12/29

Randomized | 10 Questions per Test | 20 Minutes | 70% to pass

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4.8

Quiz

Question 1/101/10

This type of analysis?

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1 correct answer

Identifies patterns in the distribution of attention across tokens

Provides exact numerical values of attention weights

Optimizes hyperparameters automatically

Shows the model’s overall accuracy

Quiz

Question 2/102/10

You are working on a project that requires a language model to perform well across multiple languages, including low-resource languages. The model needs to be deployed on a cloud infrastructure that charges based on the number of parameters and inference time. Which approach would be the most effective for selecting an appropriate model, considering both multilingual support and cost efficiency?

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1 correct answer

Select T5-Multilingual and fine-tune on a few selected languages

Use a separate smaller model for each language

Use GPT-3 with fine-tuning on each specific language

Choose mBERT (Multilingual BERT) and perform task-specific fine-tuning

Quiz

Question 3/103/10

Which of the following data preparation tasks is most effectively handled using cuDF on the GPU before feeding the data into a machine learning model?

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1 correct answer

Running a basic text search on a small dataset

Loading data into a pandas DataFrame and then transferring it to the GPU

Sorting a small dataset of less than a thousand rows

Performing complex joins and aggregations on a large dataset with millions of rows

CuDF is optimized for handling large datasets on the GPU, and it excels at performing complex joins and aggregations, which can significantly speed up data preparation processes. Which of the following data manipulation tasks would benefit the most from GPU acceleration? A. Running a simple arithmetic operation on a small dataset B. Loading a dataset from a database into memory C. Sorting a large dataset of millions of rows D. Executing a basic text search on a small dataset Option C is correct. Sorting a large dataset of millions of rows benefits from GPU acceleration due to the high computational demands, which GPUs can handle more efficiently than CPUs. When using iterative prompt engineering to refine the behavior of a generative AI model, which approach is most effective in aligning the model’s output with your specific intentions? A. Focus on making the prompt as concise as possible to avoid confusing the model. B. Use a highly detailed prompt from the start to avoid unnecessary iterations. C. Allow the model to generate multiple outputs from the same prompt to compare results. D. Start with a broad prompt and gradually add specific instructions based on the output. Option D is correct. Starting with a broad prompt and gradually adding specific instructions allows for iterative refinement based on the model’s output, leading to better alignment with the desired behavior. You are conducting an A/B test to evaluate the effectiveness of different prompt strategies for a generative AI model used in a customer support chatbot. The goal is to determine which prompt produces more accurate and helpful responses. However, the test results show only a slight difference in user satisfaction between the two prompt strategies. Which approach would be most effective in refining your A/B test to yield more actionable insights? A. Use the same prompts but vary the model architecture to test different generative AI models B. Increase the number of users participating in the A/B test without changing the prompts C. Introduce a third prompt that combines elements of the first two and evaluate it alongside the original prompts D. Abandon the A/B test and implement both prompts in production, letting the model choose dynamically Option C is correct.

Right Answer: D

Quiz

Question 4/104/10

Option D is correct. Explanation: Introducing a third prompt that combines elements from the first two strategies allows for a more nuanced comparison and can help identify a more effective approach by blending successful aspects of the original prompts. You are developing a system that processes large volumes of legal documents to extract named entities such as company names, contract dates, and legal terms. The senior developer asks you to choose the most appropriate Python package to handle this task efficiently, with a focus on accuracy and performance. Which Python package would be most appropriate for implementing named entity recognition (NER) in this scenario?

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1 correct answer

spaCy

Pandas

Scikit-learn

NumPy

SpaCy is a powerful and efficient Python package for named entity recognition (NER), providing high accuracy and performance for processing and extracting entities from text. You have trained two different generative AI models, Model X and Model Y, on the same dataset. To compare their performance, you calculate the Mean Squared Error (MSE) and the proportion of explained variance (R²). Model X has a lower MSE but also a lower R² compared to Model Y. Which model is likely better at explaining the variability in the data? A. Model Y, because it has a higher MSE B. Model X, because it has a lower MSE C. Model X, because a lower MSE always indicates a better model D. Model Y, because it has a higher R² Option D is correct. A higher R² indicates that Model Y better explains the variability in the data, even though it has a higher MSE. R² provides insight into how well the model captures the data’s structure. You are analyzing a dataset containing customer purchase data from an online retailer to build a predictive model for customer churn. The dataset contains categorical variables, missing values, and some features with highly skewed distributions. Which combination of steps would best prepare the data for accurate modeling? A. Use mean imputation for missing numerical values, manually bin categorical variables, and ignore skewed distributions during the modeling process. B. Replace missing values with zeros, use min-max scaling for all features, and remove features with skewed distributions. C. Impute missing values using the mode for categorical variables, apply one-hot encoding for categorical features, and log-transform highly skewed numerical features. D. Drop all rows with missing values, use label encoding for categorical features, and standardize all numerical features using z-score normalization. Option C is correct. Imputing missing values using the mode for categorical variables, applying one-hot encoding, and log- transforming skewed features ensures that the data is clean, appropriately encoded, and normalized, improving model performance.

Right Answer: A

Quiz

Question 5/105/10

You are tasked with developing a generative AI model for a client that needs to generate creative marketing content based on customer behavior data. The data includes customer purchase history, browsing patterns, and social media interactions. Which of the following approaches would be most effective in ensuring that the generated content is both relevant and creative? (Select two)

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1 correct answer

Leverage unsupervised learning to identify clusters in customer data and use these clusters to guide content generation.

Fine-tune a pre-trained Large Language Model (LLM) on the specific customer behavior dataset.

Use transfer learning to apply insights from a different but similar domain to the customer behavior data.

Use a rule-based system to generate content based on predefined templates.

Employ a reinforcement learning framework to dynamically adjust the content based on real-time customer feedback.

Quiz

Question 6/106/10

You have been asked to analyze the performance of a set of LLM models deployed across different regions. The key metrics include response accuracy, latency, and user satisfaction. You need to present your findings in a way that clearly shows regional differences in these metrics to help guide future deployment strategies. Which visualization technique would best allow stakeholders to compare performance metrics across different regions?

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1 correct answer

Create a grouped bar chart to compare each metric across regions.

Use a pie chart to represent the proportion of metrics for each region.

Use a scatter plot to map each region's performance across multiple metrics.

Generate a line chart showing metric trends over time for each region.

Quiz

Question 7/107/10

During the deployment of a large-scale LLM for real-time translation services, you observe that the model's performance degrades significantly when deployed on different geographic servers. Which of the following is the most likely reason for this performance inconsistency?

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1 correct answer

Variation in local dialects and accents

Differences in server hardware specifications

Insufficient number of training epochs

Latency due to network distance from the central server

Quiz

Question 8/108/10

A multinational e-commerce company is implementing a generative AI model to automatically generate personalized product recommendations based on customer browsing history. What is the most important factor to ensure the recommendations are relevant and personalized?

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1 correct answer

Focusing on the speed of generating recommendations by optimizing model inference time.

Incorporating feature engineering to extract user preferences and patterns from browsing history before training the model.

Using a large, general dataset of product interactions from various industries.

Deploying the model on a distributed computing system for scalability.

Feature engineering helps extract meaningful patterns from customer browsing history, which ensures that the recommendations are relevant and aligned with user preferences. You are curating a dataset for a retrieval-augmented generation (RAG) system designed to provide technical support by generating solutions based on a repository of troubleshooting guides and user manuals. During the embedding process, you notice that similar technical issues are mapped to significantly different vectors, leading to inconsistent retrieval results. What is the most effective strategy to address this issue? A. Fine-tune the embedding model specifically on the troubleshooting guides B. Normalize the text by removing all technical terms C. Increase the size of the training dataset D. Use a different pre-trained language model without fine-tuning Option A is correct. Fine-tuning the embedding model on the specific troubleshooting guides will ensure that similar issues are represented similarly in the vector space, leading to more consistent retrieval results. You are tasked with scaling a Large Language Model (LLM) for a multilingual application where the model needs to handle multiple languages without significant degradation in performance. Given limited computational resources, which approach would be the most effective? A. Use a single LLM with shared parameters across languages B. Apply LoRA (Low-Rank Adaptation) to add language-specific modules C. Deploy a mixture of experts (MoE) model for each language D. Train separate models for each language Option A is correct. Using a single LLM with shared parameters across languages allows the model to efficiently handle multiple languages without requiring a large amount of computational resources. You are reading a research paper on the latest advancements in large language models (LLMs). The paper highlights a novel technique that improves the model's ability to generate coherent long-form text by addressing the issue of context fragmentation. Which of the following techniques is most likely being discussed? A. Data augmentation for expanding the training set B. Dropout for regularization C. Transformer-XL for extended context D. Knowledge distillation for model compression

Right Answer: A

Quiz

Question 9/109/10

Option D is correct. Explanation: Transformer-XL addresses the issue of context fragmentation by enabling models to retain longer-term dependencies, which allows for more coherent long-form text generation. A customer service department uses a Large Language Model (LLM) to analyze and categorize incoming support emails into various topics like billing, technical issues, and product information. However, the model struggles with correctly categorizing emails that contain jargon or ambiguous terms. What is the best approach to improve the model’s categorization performance?

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1 correct answer

Using a rule-based system in parallel to the LLM for handling emails with ambiguous terms.

Retraining the LLM from scratch with a more extensive vocabulary.

Fine-tuning the LLM on a labeled dataset that includes examples of emails containing jargon and ambiguous terms.

Increasing the batch size during training to speed up the learning process.

Quiz

Question 10/1010/10

You are developing a generative AI model that needs to generate high-quality images from textual descriptions in real-time. Which two of the following approaches will best optimize the performance and quality of your model given the hardware constraints of a GPU with limited memory? (Select two)

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1 correct answer

Use gradient checkpointing

Implement data parallelism across multiple GPUs

Increase the batch size to the maximum the GPU can handle

Implement mixed precision training

Use a larger learning rate to converge faster

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Quiz name:NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL)
Total number of questions:140
Number of questions for the test:50
Pass score:80%

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