NCA-GENL Practice Test 2 — 10 Questions

Question 1

What is the primary function of the 'Attention' mechanism in Transformer models?

Accepted Answer

To enable the model to weigh the importance of different input tokens. The attention mechanism allows the model to compute weights that signify the relevance of different parts of the input sequence to one another, regardless of their distance. This captures long-range dependencies effectively, which traditional RNNs struggle with due to vanishing gradients over long sequences. Mastering this concept is key to understanding why transformers have become the dominant architecture for nearly all state-of-the-art generative AI and natural language processing applications.

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To reduce the number of parameters in the model

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To convert text into numerical vectors

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To perform sequential processing of inputs

Question 2

What is the role of 'Temperature' in the context of LLM text generation?

Accepted Answer

It adjusts the probability distribution before sampling the next token.. Temperature controls the randomness of the model's output distribution. A low temperature makes the model more confident and deterministic by sharpening the probability distribution, favoring the most likely next token. Conversely, a high temperature flattens the distribution, increasing the likelihood of selecting less probable tokens. This parameter is crucial for balancing creativity and coherence in generative AI applications, allowing users to fine-tune the model's output behavior.

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It regulates the cooling of the GPU hardware.

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It determines the maximum length of the generated sequence.

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It compresses the model weights to save memory.

Question 3

An organization is concerned about 'Model Drift' affecting the trustworthiness of their customer-facing chatbot. What is the most effective way to monitor and address this issue?

Accepted Answer

Implement continuous evaluation metrics to detect performance degradation.. Model drift occurs when the model's performance degrades over time because the real-world environment changes, making the training data obsolete. Trustworthy AI requires continuous monitoring to detect these performance shifts. By establishing an evaluation pipeline that periodically tests the model against current benchmarks, organizations can identify drift early and trigger retraining, ensuring the system remains accurate, relevant, and reliable in the face of changing user behaviors.

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Hard-code all possible responses to prevent the model from learning new patterns.

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Increase the number of parameters in the model to improve its reasoning capacity.

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Disable the model's feedback collection to avoid processing incorrect user data.

Question 4

You are performing a comparative analysis of two different LLM architectures by visualizing their performance on a RAG (Retrieval-Augmented Generation) benchmark. Which visualization is best for comparing the distributions of answer accuracy scores?

Accepted Answer

Box plot comparing accuracy scores.. Box plots (or box-and-whisker plots) provide a compact summary of data distribution, including median, quartiles, and outliers. When comparing two architectures, they allow for an immediate visual assessment of consistency, range, and bias. This is crucial in RAG benchmarking because high accuracy is insufficient; developers need models that consistently perform well across diverse queries, and box plots reveal whether one architecture suffers from more frequent low-quality outliers than the other.

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Stacked bar chart of token counts.

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Radial plot of training time.

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Individual data point scatter plot.

Question 5

Refer to the exhibit. An engineer is tuning a deployment config. Why is 'enable_cuda_graph' set to true in this JSON configuration?

Accepted Answer

To reduce CPU overhead during repetitive kernel launches.. CUDA Graphs capture a sequence of GPU work as a single graph, reducing CPU overhead associated with kernel launches. This is critical for LLMs where many small kernel calls can lead to CPU-bound execution. In high-performance generative AI scenarios, reducing launch latency is essential to ensure that the GPU remains saturated with work, thereby maximizing tokens-per-second and reducing total request latency for end-users.

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To increase the maximum batch size to 256.

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To convert the model precision from fp16 to fp8.

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To force the model to use the CPU for inference.

Question 6

A bank uses an NVIDIA NIM microservice to host an LLM for loan pre-screening. Before go-live, the risk team must confirm that the model's outputs are reproducible and that any change in behavior can be traced to a specific model version. Which deployment practice best satisfies this requirement?

Accepted Answer

Pin the NIM container to an immutable image digest and record the model name, digest, and inference parameters in a model registry entry for each release.. Reproducibility and traceability require freezing the exact serving artifact and recording it. An immutable image digest locks the weights, tokenizer, and runtime, while a registry entry maps each release to that digest and its inference parameters. Logging, parallelism, or higher temperature do not bind an output to a specific model version, so they cannot satisfy an audit that must reconstruct which model produced a decision.

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Run the model on two GPUs in a tensor-parallel configuration so responses are identical across replicas.

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Enable verbose prompt logging on the NIM endpoint and retain the logs for 30 days.

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Increase the model's temperature setting so the risk team can observe a wider range of outputs before approving the release.

Question 7

What is the primary role of 'Loss Scaling' when training deep learning models in FP16 precision?

Accepted Answer

To prevent gradient underflow in FP16 training. Loss scaling is essential because FP16 has a narrower dynamic range than FP32. Small gradient values can underflow to zero, causing the model to stop learning. By scaling the loss up before backpropagation, the gradients are kept within the representable range of FP16, and then scaled back down during the weight update, ensuring stable and effective training while maintaining the speed advantages of half-precision compute.

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To increase the training speed of the GPU

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To reduce the required GPU memory footprint

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To normalize the input data distribution

Question 8

A team wants to load and run an optimized quantized LLM entirely inside a Python application with minimal dependencies, using a single high-level API that handles engine building and generation. They are not deploying a network service. Which component of the NVIDIA software stack is designed for this use case?

Accepted Answer

NVIDIA TensorRT-LLM's Python LLM API, which wraps engine build and generation behind a high-level runtime interface.. When the goal is to run an optimized, quantized LLM inside a Python process without standing up a service, the TensorRT-LLM Python LLM API is the fit. It abstracts engine building and token generation behind a high-level interface, avoiding client-server overhead. Serving platforms such as Triton or NIM and preprocessing libraries such as DALI solve different problems and would add unnecessary infrastructure or miss the requirement.

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NVIDIA NIM microservices, which package models as containerized endpoints with standardized inference APIs.

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NVIDIA DALI, which provides a data loading and augmentation library primarily for computer vision preprocessing pipelines.

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NVIDIA Triton Inference Server, which loads models from a model repository and exposes HTTP and gRPC endpoints.

Question 9

A researcher is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) techniques. Which method is specifically designed to inject trainable low-rank matrices into the transformer layers to reduce the number of trainable parameters?

Accepted Answer

LoRA. Low-Rank Adaptation (LoRA) is the standard PEFT method for injecting trainable matrices into frozen pre-trained models. By minimizing the number of updated parameters, it significantly reduces VRAM requirements and storage overhead during the fine-tuning process. This technique is vital for deploying custom LLMs on resource-constrained hardware, as it maintains model performance while drastically simplifying the memory demands of the training pipeline.

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Prefix Tuning

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Prompt Tuning

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Full Fine-Tuning

Question 10

A team is fine-tuning an LLM and wants to detect whether individual training examples are causing unusually large gradient updates. They plan to visualize per-example gradient norms alongside other diagnostics. Which two visualizations are most appropriate for identifying these influential examples? (Choose two.)

Accepted Answer

A sorted bar chart of per-example gradient norms with examples ranked from largest to smallest. Detecting influential examples requires per-example gradient information. A sorted bar chart ranks examples so the largest norms stand out, while a scatter plot of gradient norm versus loss adds context about why those norms are large. Together they let the team isolate and inspect the examples driving unusually large updates.

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A line chart of the moving average of total training loss across epochs

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A pie chart showing the proportion of examples in each loss decile

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A heatmap of the model's attention weights for a single randomly chosen example