NCP-GENL Practice Test 9 — 10 Questions

Question 1

Refer to the exhibit. What is the implication of setting the memory_limit to 0.8 in the context of an LLM inference service?

Accepted Answer

It will evict old KV cache entries when 80% limit is reached.. A memory limit of 0.8 indicates that the system will reserve up to 80% of the allocated memory for the KV cache. Once this limit is reached, the Least Recently Used (LRU) policy will begin evicting older sequences to make room for new ones. This helps prevent hard OOM crashes, but users of the evicted sequences will experience errors or forced re-computations when trying to continue their generation tasks.

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It guarantees that the system will never crash due to memory.

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It forces the GPU to run at 80% of its clock speed.

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It expands the memory capacity by 20% using swap space.

Question 2

Which of the following describes the 'Chain-of-Verification' (CoVe) prompting technique?

Accepted Answer

A process where the model checks its own claims for factual consistency.. Chain-of-Verification is a sophisticated technique designed to reduce hallucinations. It prompts the model to generate a response, then draft questions to verify its own claims, answer those questions, and finally revise the original response based on the verification. This is highly effective for NVIDIA developers building high-stakes applications where factual accuracy is non-negotiable and automated auditing is required.

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A method to verify that the GPU is running at full capacity.

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A technique to optimize the model's weights during training.

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A protocol for securing the prompt against injection attacks.

Question 3

Which TWO of the following practices are considered standard procedures for preparing a dataset for Instruction Fine-Tuning (IFT)? (Choose two)

Accepted Answer

Removing duplicate or highly redundant instructional examples. Instruction fine-tuning requires high-quality, diverse, and well-formatted data to ensure the model learns to follow specific user prompts. Removing duplicates prevents the model from over-relying on single examples, while consistent formatting (like ChatML or Alpaca format) ensures the model learns the structural cues for turn-based conversation, which is fundamental for effective performance in downstream instruction-following tasks.

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Applying aggressive token truncation to all training samples

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Converting all text into numerical vectors before training

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Increasing the learning rate by a factor of 100

Question 4

Which technique is most appropriate for a task requiring an LLM to generate code in a specific enterprise-internal syntax that is not well-represented in its public training data?

Accepted Answer

Few-shot prompting with multiple code examples.. When dealing with proprietary or rare syntax, few-shot prompting with high-quality, representative examples is the most effective way to guide the model. By including these examples within the prompt, you provide the context the model lacks, significantly reducing syntax errors. This is a critical skill for NVIDIA developers building custom coding assistants for internal proprietary frameworks or legacy infrastructure support.

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Zero-shot prompting with broad general coding instructions.

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Increasing the model temperature to encourage exploration.

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Reducing the context window to force brevity.

Question 5

Which component in the NVIDIA NeMo framework is specifically designed to manage the configuration and orchestration of large-scale fine-tuning jobs?

Accepted Answer

NeMo Framework Launcher. The NeMo Framework Launcher is the dedicated tool for managing multi-node, large-scale training jobs. It abstracts the complexities of cluster scheduling, environment setup, and hyperparameter management. By using the launcher, engineers ensure that fine-tuning tasks are executed efficiently across NVIDIA compute clusters, allowing for reproducible and scalable experiments that align with enterprise-grade development standards and best practices for large model management.

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TensorRT-LLM

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NVIDIA Triton Inference Server

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CUDA Toolkit

Question 6

Refer to the exhibit. An engineer observes that a model fine-tuned with this LoRA configuration is failing to converge on a highly complex legal document domain. What is the most likely cause of this issue?

Accepted Answer

The rank value is too low to represent the complex patterns of the domain.. The rank of 8 is likely too low for capturing the intricate, nuanced patterns required for legal documentation. While low-rank adaptation is efficient, complex domains often necessitate higher rank values to provide enough expressive capacity within the trainable adapter layers. This configuration mismatch limits the model's ability to learn the specific syntactic and semantic structures inherent in specialized legal texts, leading to poor convergence and inadequate performance.

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The dropout value is set too high, causing the model to underfit.

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The target modules are incorrectly specified, leading to a loss of attention.

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The alpha parameter is too high, causing gradient explosion.

Question 7

Refer to the exhibit. What is the intended outcome of this data cleaning configuration for a Large Language Model pre-training corpus?

Accepted Answer

It optimizes for the removal of low-quality or nonsensical text while minimizing redundancy.. This configuration aims to remove low-quality text that fails to meet minimum length requirements or exhibits high perplexity (indicating gibberish or low coherence). Simultaneously, MinHash deduplication identifies and removes near-duplicate documents exceeding a 95% similarity threshold. This cleanup process is vital for pre-training, as it filters out low-value, noisy data that could impede model convergence and general quality during the training cycle.

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It specifically targets the removal of personally identifiable information (PII).

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It enforces a strict length-based chunking strategy for all documents.

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It converts all text to a vector space representation before filtering.

Question 8

Refer to the exhibit. What is the impact of this filter on the training corpus?

Accepted Answer

It discards documents that are overly repetitive or lack linguistic richness.. This filter removes documents with low lexical diversity, which often contain repetitive, low-value, or 'boilerplate' text. Such content provides little signal for the model to learn meaningful language patterns. By enforcing a minimum diversity threshold, you ensure the corpus consists of richer, more informative language, which typically leads to better convergence and higher quality output in the trained model.

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It limits the vocabulary size to 20% of the original content.

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It forces the model to use 20% more computation for tokenization.

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It reduces the training corpus size by exactly 20%.

Question 9

When deploying an LLM using NVIDIA TensorRT-LLM, what is the primary benefit of pre-compiling the model into a TensorRT engine?

Accepted Answer

It enables layer fusion and kernel selection for the target GPU.. Pre-compiling with TensorRT-LLM allows for layer fusion, kernel auto-tuning, and memory optimization tailored specifically to the target GPU architecture. By performing these heavy optimizations offline, the inference engine can execute at peak performance immediately upon loading. This eliminates the runtime overhead associated with graph compilation or dynamic graph execution, ensuring minimal latency and optimal resource utilization from the very first inference request.

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It allows the model to run on any generic CPU architecture.

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It eliminates the need for any GPU memory during inference.

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It automatically scales the model across multiple distributed nodes.

Question 10

An organization is deploying a high-throughput LLM on NVIDIA Triton Inference Server. They observe significant tail latency spikes when serving multiple concurrent requests. Which strategy most effectively optimizes GPU utilization and reduces latency jitter for these concurrent model instances?

Accepted Answer

Enable dynamic batching with an optimized max_queue_delay_microseconds setting.. Dynamic Batching is the primary mechanism in Triton to combine individual inference requests into a single batch, significantly improving throughput while minimizing latency. By configuring the 'max_queue_delay_microseconds' parameter, the system balances wait times with compute efficiency. This is critical for enterprise deployments where maximizing GPU hardware investment while maintaining strict service-level agreements is the standard requirement for production-grade generative AI applications.

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Increase the number of CPU threads per model instance in the configuration file.

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Disable all model instances to ensure requests are processed in serial order.

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Switch the model to a lower precision format like FP8 without model recalibration.