NCP-GENL Practice Test 2 — 10 Questions

Question 1

When fine-tuning a Large Language Model using Low-Rank Adaptation (LoRA), which architectural component is primarily modified to reduce computational overhead while maintaining performance?

Accepted Answer

The attention weight projection matrices. LoRA freezes the pre-trained model weights and injects trainable rank decomposition matrices into the transformer architecture layers. By targeting specifically the attention query, key, and value projection matrices, practitioners can achieve high performance with a fraction of the trainable parameters. This approach is critical for memory-constrained environments, allowing fine-tuning on consumer-grade NVIDIA GPUs while avoiding the massive memory requirements associated with full parameter updates.

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The entire embedding layer matrix

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The feed-forward network activation functions

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The entire decoder hidden state output

Question 2

When optimizing a model using NVIDIA TensorRT, what is the primary benefit of enabling 'layer fusion' during the optimization process?

Accepted Answer

It reduces global memory access overhead. Layer fusion reduces the overhead of launching multiple small GPU kernels by combining them into a single, optimized kernel. This strategy minimizes the total number of reads and writes to global VRAM, which is often the bottleneck in modern deep learning. By keeping intermediate data in high-speed registers or shared memory, the model spends less time waiting for memory access and more time performing actual floating-point operations.

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It increases the overall model weight precision

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It automatically prunes redundant parameters

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It replaces floating-point math with integer math

Question 3

A team is deploying a 13B-parameter decoder-only LLM on a single NVIDIA A100 40GB GPU for a real-time chatbot. During load, the process runs out of memory even though the model weights in FP16 require roughly 26GB. The team wants to reduce GPU memory usage with minimal impact on output quality and no change to the model architecture. Which technique is most appropriate?

Accepted Answer

Apply INT8 weight-only quantization using NVIDIA TensorRT-LLM. The bottleneck is static weight storage on a single 40GB device. Weight-only INT8 quantization halves the parameter footprint while preserving the architecture and offering strong quality retention, and TensorRT-LLM provides a supported path for this on NVIDIA hardware. The other choices target attention compute, KV cache paging, or multi-GPU partitioning, none of which reduce the resident weight memory on one GPU.

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Enable tensor parallelism across the single GPU's SMs

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Switch the attention implementation to FlashAttention-2

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Increase the KV cache block size to 32 tokens

Question 4

An LLM inference service on NVIDIA Triton Inference Server is experiencing occasional out-of-memory (OOM) errors on the GPU. The team wants to monitor GPU memory usage to predict and prevent OOM conditions. Which NVIDIA tool provides real-time GPU memory metrics that can be integrated with Prometheus for alerting?

Accepted Answer

NVIDIA Data Center GPU Manager (DCGM). DCGM is designed for data center GPU monitoring and provides comprehensive metrics, including memory usage. With DCGM Exporter, these metrics can be scraped by Prometheus, enabling real-time alerts when memory usage approaches limits. This proactive monitoring allows the team to adjust batch sizes or resource allocation before OOM errors occur, ensuring reliable LLM inference.

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NVIDIA Nsight Systems

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NVIDIA Triton Inference Server metrics endpoint

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NVIDIA System Management Interface (nvidia-smi)

Question 5

What is the primary role of a Model Repository in the NVIDIA Triton Inference Server architecture?

Accepted Answer

To act as a centralized filesystem for serving multiple models.. The Model Repository acts as a centralized storage location for all deployed models, their configurations, and their versions. Triton monitors this directory to detect when new versions are added or updated, allowing for seamless model updates without restarting the server. This design supports robust MLOps practices by decoupling the model storage from the inference engine runtime, ensuring that deployments remain manageable and version-controlled.

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To store training datasets for real-time model retraining.

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To handle network traffic load balancing between server nodes.

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To compile the model into an optimized executable format.

Question 6

Which TWO of the following techniques are best suited for reducing the latency of LLM inference on NVIDIA GPUs?

Accepted Answer

KV cache quantization.. Reducing LLM latency requires optimizing both the compute throughput and the memory access patterns. Key-Value (KV) cache quantization and PagedAttention are industry standards for LLM acceleration. KV cache quantization reduces memory bandwidth usage, while PagedAttention manages memory dynamically, preventing fragmentation. These methods significantly improve the token generation rate, which is the primary metric for user-perceived performance in large-scale generative AI deployments.

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Increasing the number of CPU worker threads.

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Disabling ECC memory on the GPU.

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Switching from Tensor Cores to CUDA Cores.

Question 7

When preparing a proprietary technical manual dataset for a RAG pipeline, which data preprocessing step is most critical to ensure the LLM avoids hallucinations regarding specific product configurations?

Accepted Answer

Implementing document-aware recursive character splitting with overlapping segments and metadata tagging.. Chunking strategies and metadata tagging ensure that context retrieval is precise. By preserving technical hierarchy and associating data with specific product versions, the LLM retrieves ground-truth documentation rather than generic information. This reduces hallucinations by constraining the search space to relevant, version-controlled text blocks, directly impacting the accuracy and reliability of downstream inference tasks in enterprise NVIDIA-based AI deployments.

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Converting all text to lowercase to ensure uniformity in vector embeddings.

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Performing aggressive stop-word removal to reduce the dimensionality of the vector space.

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Applying basic sentence tokenization based strictly on periods to create uniform chunks.

Question 8

What is the primary purpose of 'Few-Shot Prompting' in the context of LLM optimization?

Accepted Answer

To provide in-context learning examples to guide output.. Few-shot prompting involves providing a few examples of input-output pairs within the prompt to guide the model's performance on a specific task. This approach helps the model learn the desired format, tone, and logic without the need for intensive fine-tuning. It is a highly efficient way to steer model behavior for specific enterprise use cases, ensuring consistent results across multiple interaction sessions.

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To reduce the latency of the underlying GPU cluster.

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To compress the model weights for deployment.

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To permanently store data in the model's internal memory.

Question 9

Which TWO actions should be part of a robust incident response plan for an LLM deployment failing in production?

Accepted Answer

Rolling back to a previous stable model version. A robust incident response plan focuses on rapid mitigation and root cause analysis. Immediately reverting to a known good version reduces the impact on users, while analyzing logs and telemetry provides the necessary data to understand the failure. These steps ensure that service is restored quickly, which is the primary objective of reliability engineering in generative AI systems serving critical user traffic.

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Deploying the model to more GPUs immediately

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Turning off all security monitoring systems

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Forcing a reboot of all servers

Question 10

A research team wants to train a large decoder-only LLM where each token's representation is computed independently of token order, then inject order information afterward. They are choosing between learned absolute positional embeddings and sinusoidal absolute positional embeddings. Which statement accurately characterizes the tradeoff they face?

Accepted Answer

Sinusoidal embeddings are parameter-free and can be computed for any position, while learned embeddings require a fixed maximum length and add parameters.. Sinusoidal absolute positional embeddings are deterministic functions of position, adding no parameters and allowing evaluation at any index. Learned absolute embeddings allocate a trainable vector per position, consuming parameters and capping usable length at the trained maximum. The other options either reverse the extrapolation behavior or misattribute relative-offset abilities to learned absolute embeddings.

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Learned embeddings are strictly better because they allow the model to discover relative offsets between tokens.

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Learned embeddings can extrapolate to sequence lengths longer than those seen in training, whereas sinusoidal embeddings cannot.

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Sinusoidal embeddings must be recomputed for every batch, which makes them slower than learned embeddings at inference.