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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)

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/ 10

Quiz

1/10
You are analyzing a large dataset to identify fraudulent transactions for an online payment platform. The
dataset is highly imbalanced, with very few fraudulent cases compared to legitimate ones. Which
technique would best help in extracting meaningful insights and improving the detection of fraudulent
transactions?
Select the answer
1 correct answer
A.
Apply k-means clustering to separate fraudulent and legitimate transactions
B.
Rely on accuracy as the primary metric for model evaluation
C.
Use linear regression to predict the likelihood of fraud
D.
Use SMOTE (Synthetic Minority Over-sampling Technique) before applying classification models

Quiz

2/10
Your team is tasked with building a neural network model using Keras to predict customer churn based
on historical data. You decide to use NumPy for data preprocessing. Which approach is most
appropriate for ensuring the model is trained effectively?
Select the answer
1 correct answer
A.
Utilizing NumPy to convert categorical variables into integers and feeding them directly into the network.
B.
Using NumPy to create polynomial features from the input data before feeding them into the model.
C.
Using NumPy to randomly shuffle the data before splitting it into training and test sets.
D.
Normalizing the input data using NumPy before feeding it into the neural network.

Quiz

3/10
You have used a specialized visualization tool to create a heatmap that represents the attention weights
of different tokens in a generative AI model's output. What is the primary benefit of using a heatmap for
this type of analysis?
Select the answer
1 correct answer
A.
Identifies patterns in the distribution of attention across tokens
B.
Provides exact numerical values of attention weights
C.
Optimizes hyperparameters automatically
D.
Shows the model’s overall accuracy

Quiz

4/10
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?
Select the answer
1 correct answer
A.
Select T5-Multilingual and fine-tune on a few selected languages
B.
Use a separate smaller model for each language
C.
Use GPT-3 with fine-tuning on each specific language
D.
Choose mBERT (Multilingual BERT) and perform task-specific fine-tuning

Quiz

5/10
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?
Select the answer
1 correct answer
A.
Running a basic text search on a small dataset
B.
Loading data into a pandas DataFrame and then transferring it to the GPU
C.
Sorting a small dataset of less than a thousand rows
D.
Performing complex joins and aggregations on a large dataset with millions of rows

Quiz

6/10
Which of the following data manipulation tasks would benefit the most from GPU acceleration?
Select the answer
1 correct answer
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

Quiz

7/10
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?
Select the answer
1 correct answer
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.

Quiz

8/10
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?
Select the answer
1 correct answer
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

Quiz

9/10
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?
Select the answer
1 correct answer
A.
spaCy
B.
Pandas
C.
Scikit-learn
D.
NumPy

Quiz

10/10
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?
Select the answer
1 correct answer
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
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  • Quiz name:NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL)
  • Total number of questions:359
  • Number of questions for the test:50
  • Pass score:80%

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