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NCA-GENL Practice Questions

NVIDIA Generative AI LLMs

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Total Questions : 95

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Question # 11

Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

Options:

A.  

Quantization might help in saving power and reducing heat production.

B.  

It consists of removing a quantity of weights whose values are zero.

C.  

It leads to a substantial loss of model accuracy.

D.  

Helps reduce memory requirements and achieve better cache utilization.

E.  

It only involves reducing the number of bits of the parameters.

Discussion 0
Question # 12

What are the main advantages of instructed large language models over traditional, small language models (< 300M parameters)? (Pick the 2 correct responses)

Options:

A.  

Trained without the need for labeled data.

B.  

Smaller latency, higher throughput.

C.  

It is easier to explain the predictions.

D.  

Cheaper computational costs during inference.

E.  

Single generic model can do more than one task.

Discussion 0
Question # 13

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

Options:

A.  

Ensuring that CPUs are running at full clock speed.

B.  

Subsampling datasets to provide rapid but approximate answers.

C.  

Using the GPU for parallel processing of data.

D.  

Enabling data processing to scale to multiple GPUs.

E.  

Providing more memory for data analysis.

Discussion 0
Question # 14

In the development of Trustworthy AI, what is the significance of ‘Certification’ as a principle?

Options:

A.  

It ensures that AI systems are transparent in their decision-making processes.

B.  

It requires AI systems to be developed with an ethical consideration for societal impacts.

C.  

It involves verifying that AI models are fit for their intended purpose according to regional or industry-specific standards.

D.  

It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

Discussion 0
Question # 15

When implementing data parallel training, which of the following considerations needs to be taken into account?

Options:

A.  

The model weights are synced across all processes/devices only at the end of every epoch.

B.  

A master-worker method for syncing the weights across different processes is desirable due to its scalability.

C.  

A ring all-reduce is an efficient algorithm for syncing the weights across different processes/devices.

D.  

The model weights are kept independent for as long as possible increasing the model exploration.

Discussion 0
Question # 16

When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

Options:

A.  

Automatic parallelization of Python code across CPU cores.

B.  

GPU-accelerated columnar data processing with zero-copy memory access.

C.  

Integration with cloud-based storage for distributed data access.

D.  

Conversion of Pandas DataFrames to SQL tables for faster querying.

Discussion 0
Question # 17

Which of the following tasks is a primary application of XGBoost and cuML?

Options:

A.  

Inspecting, cleansing, and transforming data

B.  

Performing GPU-accelerated machine learning tasks

C.  

Training deep learning models

D.  

Data visualization and analysis

Discussion 0
Question # 18

In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

Options:

A.  

Model size

B.  

Accuracy on a validation set

C.  

Training duration

D.  

Number of layers

Discussion 0
Question # 19

In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

Options:

A.  

Q, K, and V are randomly initialized weight matrices used for positional encoding.

B.  

K is responsible for computing the attention scores between the query and key vectors.

C.  

Q represents the query vector used to retrieve relevant information from the input sequence.

D.  

V is used to calculate the positional embeddings for each token in the input sequence.

Discussion 0
Question # 20

In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

Options:

A.  

To optimize the model’s inference speed for production deployment.

B.  

To identify and mitigate potential biases, safety risks, and harmful outputs.

C.  

To increase the model’s parameter count for better performance.

D.  

To automate the collection of training data for fine-tuning.

Discussion 0
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