NCP-GENL Multiple Select — 50 Questions

Question 1

When profiling an application with NVIDIA Nsight Systems, which TWO metrics are most critical to identify if an application is limited by the PCIe bus?

Accepted Answer

Host-to-Device (H2D) throughput.. PCIe bus saturation occurs when the transfer rate of data between the host and the GPU becomes a bottleneck. By monitoring the H2D (Host-to-Device) and D2H (Device-to-Host) transfer metrics, developers can see if data movement consumes a disproportionate amount of execution time. Identifying these spikes is crucial because offloading data to the GPU is often the slowest part of a pipeline, and minimizing these transfers is key to scaling high-performance AI.

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GPU Register usage count.

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Shared memory bank conflict count.

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SM clock frequency.

Question 2

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 3

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 4

When building an NVIDIA NeMo LLM application for automated document review, which THREE of the following prompt design choices are critical for ensuring high-quality output? (Select exactly THREE)

Accepted Answer

Use standardized delimiters to isolate user-provided documents.. Effective document review requires accuracy, traceability, and consistency. Using clear delimitation prevents data corruption, requiring structured output (like JSON) allows for downstream programmatic integration, and chain-of-thought prompts ensure the model validates its conclusions against the text. These choices together create a robust, production-ready pipeline that minimizes errors and provides the necessary structure for automated workflows in enterprise environments.

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Include instructions that prioritize speed over accuracy.

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Use a high temperature setting to ensure diverse review perspectives.

Question 5

Which THREE architectural features are essential for enabling efficient inference of massive LLMs on multi-GPU systems?

Accepted Answer

KV Caching to store previous sequence states.. Efficient inference at scale requires techniques that address memory constraints, compute latency, and communication overhead. Model parallelism, KV caching, and weight quantization are fundamental pillars that allow large models to fit within limited memory pools while maintaining high throughput. These techniques collectively ensure that the model remains responsive and cost-effective when serving large-scale requests in a production environment.

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Batch Size reduction to increase memory throughput.

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Increasing the learning rate during inference.

Question 6

A team is optimizing an NVIDIA TensorRT-LLM deployment of a 70B model on multiple GPUs. They want to reduce inter-GPU communication overhead and improve throughput. Which two techniques should they consider? (Choose two.)

Accepted Answer

Enable pipeline parallelism to split layers into stages.. Tensor parallelism benefits from NVLink's high bandwidth to reduce all-reduce overhead, while pipeline parallelism lowers communication frequency by staging layers across GPUs. Together they address the communication bottleneck for large multi-GPU models. KV cache block size, disabling in-flight batching, and FP32 precision do not reduce inter-GPU communication and can hurt throughput.

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Disable in-flight batching to simplify scheduling.

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Use FP32 precision for all weights to improve numerical stability.

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Increase the KV cache block size to 256 tokens.

Question 7

You are preparing a dataset for supervised fine-tuning (SFT) of an NVIDIA NeMo LLM to follow instructions. Which TWO data preparation practices are essential to ensure the model learns to generalize rather than memorize? (Choose two.)

Accepted Answer

Include a diverse set of instruction types and domains in the training data rather than repeating a few templates.. Disjoint data splits prevent leakage and give honest evaluation, while diverse instruction types and domains teach the model to follow instructions generally rather than memorize templates. Together, these practices reduce overfitting and improve generalization. The other options either fail to address memorization, actively encourage it, or unnecessarily restrict the data distribution.

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Increase the learning rate significantly to force the model to escape memorization of individual examples.

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Duplicate the most common instruction-response pairs to reinforce the desired behavior.

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Use only English instructions to simplify tokenization and avoid multilingual complexity.

Question 8

Which THREE factors significantly influence the memory consumption during LLM fine-tuning? (Choose three)

Accepted Answer

The precision of the optimizer states. Memory consumption is driven by the model parameters, the optimizer states (which are often larger than the model weights), and the activations generated during the forward pass. Efficient management of these components is critical for scaling to larger models. By optimizing these three areas, practitioners can successfully fit large models into limited VRAM, ensuring stability throughout the fine-tuning process.

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The number of CPU cores available for data loading

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The version of the operating system kernel

Question 9

When assessing the quality of a dataset for instruction fine-tuning, which TWO metrics or methods are considered most reliable for measuring dataset diversity?

Accepted Answer

Embedding-based clustering to visualize topical coverage across the corpus.. Dataset diversity ensures that the model encounters a broad range of topics and linguistic patterns, which is critical for generalization. Using embedding-based clustering allows for the identification of thematic coverage, while perplexity distribution analysis helps assess whether the dataset contains a balance of common and complex structures. These methods together provide a quantitative view of the data's breadth, reducing the risk of bias or overfitting.

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Calculating the total number of words in the dataset.

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Measuring the average response length for every instruction.

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Checking if the dataset is solely in ASCII format.

Question 10

Which TWO of the following practices are recommended for ensuring ethical AI development when using NVIDIA NIMs in an enterprise environment?

Accepted Answer

Perform periodic bias audits on model responses using diverse, representative evaluation datasets.. Ensuring ethical AI involves both technical validation and process transparency. Regular bias auditing helps detect unintended stereotyping, while implementing robust logging ensures accountability for every generated output. These practices allow organizations to monitor for discriminatory patterns and maintain an audit trail for compliance, which is essential for building trust with users and adhering to global ethical standards for AI deployment in sensitive business sectors.

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Bypass local logging to optimize inference latency for high-throughput applications.

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Use the model in a closed-loop system where users cannot report offensive content.

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Exclude all metadata from model outputs to prevent potential privacy leaks.

Question 11

You are preparing a massive dataset for training a NeMo-based LLM. Which TWO data preprocessing steps are critical to prevent data leakage and ensure model quality?

Accepted Answer

Performing temporal or content-based splitting to isolate the validation set.. Data leakage occurs when test data is inadvertently included in the training set, leading to inflated performance metrics. Deduplication is equally vital, as repetitive data causes the model to memorize samples rather than generalize. In NVIDIA workflows, these steps are typically performed via distributed scripts on the cluster before tokenization, ensuring that the model learns unique, non-overlapping information across all shards.

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Increasing the frequency of data augmentation in the training pipeline.

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Adding metadata tags to all training sequences for classification.

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Converting all text to lowercase to increase vocabulary efficiency.

Question 12

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.

Answer

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 13

Which TWO factors should be prioritized when selecting a quantization strategy for deploying a large language model on constrained edge hardware? (Select TWO)

Accepted Answer

Reducing the memory footprint of the model weights.. Selecting a quantization strategy requires balancing precision loss against hardware performance gains. When deploying to edge devices, memory bandwidth and storage capacity are typically the primary bottlenecks. By reducing precision from FP16 or FP32 to INT8 or FP8, you directly decrease the model's memory footprint and increase the number of operations per clock cycle, which is essential for maintaining acceptable real-time inference speeds on resource-limited hardware.

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Maximizing the model training loss to improve generalization.

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Increasing the number of neural network layers in the architecture.

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Replacing the Transformer architecture with a linear regression model.

Question 14

You are building an evaluation harness for a retrieval-augmented generative assistant running on NVIDIA NIM microservices. The product owner wants a single trustworthy number for 'answer quality,' but you need to defend the evaluation design. Which two design choices most directly protect the evaluation from producing misleading quality scores? (Choose two.)

Accepted Answer

Hold out a prompt set that was never used during fine-tuning or prompt engineering, and keep it frozen across model versions.. Leakage control and retrieval attribution are the two structural safeguards that make RAG evaluation trustworthy. A frozen, unseen prompt set ensures score changes reflect the model, and the retrieval ablation shows whether gains come from context or from parametric memory. Composite averaging, self-grading, and elevated temperature all add noise or bias rather than removing it, so they undermine the credibility the product owner needs.

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Let the same generative model that powers the assistant also grade its own answers, since it understands the domain best.

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Average the outputs of several automatic metrics into one composite score so the product owner receives a single number.

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Increase decoding temperature during evaluation so the model explores more of the answer space and scores reflect average-case behavior.

Question 15

An engineer is reviewing the architecture of a decoder-only LLM that must support very long input contexts for document analysis. They are considering architectural choices that extend effective context length beyond what the model saw during pretraining. Which TWO techniques are designed specifically to extend usable context length without retraining the entire model from scratch? (Choose two.)

Accepted Answer

Position interpolation, which rescales positional indices so longer sequences fall within the range seen during pretraining.. Position interpolation and YaRN both operate on positional encoding to reconcile desired sequence lengths with the range seen during pretraining. Interpolation compresses indices, while YaRN adjusts rotary frequencies to improve extrapolation. Both are context-extension methods that require only light fine-tuning, unlike changes to head count, MLP activation, or vocabulary size, which do not address positional generalization.

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Switching the feed-forward activation from SwiGLU to GeLU to improve long-range dependency modeling.

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Increasing the number of attention heads while keeping the head dimension fixed.

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Reducing the vocabulary size to shorten the embedding matrix and free memory for longer sequences.

Question 16

An engineer is analyzing why a decoder-only LLM with 32,000-token context length fails to answer questions that require information from the beginning of a long document when the answer is near the end. The model was trained with standard causal attention. Which two architectural or training factors are most likely contributing to this failure? (Choose two.)

Accepted Answer

The positional encoding scheme may not generalize well beyond the sequence lengths seen during pretraining.. Long-context retrieval failures in decoder-only LLMs typically stem from two sources: pretraining on sequences much shorter than the target context, which prevents the model from learning to attend across the full window, and positional encoding schemes that do not generalize beyond training lengths. Together these cause the model to underuse information at the start of a long document. The causal mask, feed-forward network, and vocabulary size are not primary causes of this behavior.

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The feed-forward network in each layer compresses the hidden state, discarding information from early tokens.

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The vocabulary size is too small to represent the document's domain-specific terms, causing tokenization errors.

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Causal attention masks prevent each token from attending to future tokens, which limits bidirectional reasoning over the document.

Question 17

Which TWO of the following NVIDIA AI Enterprise tools are specifically designed to optimize and accelerate the deployment of LLMs in containerized environments?

Accepted Answer

NVIDIA Triton Inference Server. NVIDIA Triton Inference Server and TensorRT-LLM are the cornerstones of the NVIDIA AI Enterprise deployment stack. Triton provides a unified serving platform that abstracts the complexities of hardware management, while TensorRT-LLM provides the compiler technology to fuse kernels and quantize weights. Together, they enable developers to move from research models to production-ready services with minimal code changes, ensuring optimized performance across NVIDIA GPUs.

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NVIDIA Modulus

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NVIDIA NeMo Framework

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NVIDIA DriveWorks

Question 18

A developer is optimizing a generative AI model for inference on NVIDIA GPUs. They want to reduce memory footprint and improve throughput without sacrificing accuracy. Which two techniques should they apply? (Choose two.)

Accepted Answer

Use NVIDIA TensorRT with INT8 quantization and calibration.. INT8 quantization and structured sparsity both reduce memory footprint and improve throughput. INT8 quantization uses 8-bit integers, cutting memory usage by 4x compared to FP32, and Tensor Cores accelerate INT8 operations. Structured sparsity prunes weights in a 2:4 pattern, halving memory for weights and enabling sparse Tensor Core acceleration. Both are supported by TensorRT and maintain accuracy with proper calibration and fine-tuning.

Answer

Use NVIDIA GPUDirect Storage to offload model weights to NVMe.

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Increase the batch size to amortize memory overhead.

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Enable NVIDIA Ampere TF32 precision for matrix multiplications.

Question 19

An engineer is using NVIDIA TensorRT-LLM to optimize an LLM for inference. They want to reduce the memory footprint of the KV cache during long-context generation. Which TWO techniques are supported by TensorRT-LLM to achieve this? (Choose two.)

Accepted Answer

Enable paged KV cache with block sharing.. TensorRT-LLM provides native support for FP8 KV cache quantization and paged KV cache with block sharing. FP8 quantization halves cache memory, while paged cache with block sharing reduces duplication across sequences. Both are configuration-time features that do not require model changes. The other options involve model modifications or misapply TensorRT features, making them incorrect for this scenario.

Answer

Use TensorRT's INT8 calibration for the KV cache.

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Apply structured pruning to remove attention heads.

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Enable sliding window attention in the model architecture.

Question 20

A developer is using NVIDIA TensorRT-LLM to optimize a GPT-based model for inference. They want to reduce the model's memory footprint and improve throughput without retraining. Which two techniques can be applied during the TensorRT-LLM build process to achieve these goals? (Choose two.)

Accepted Answer

Enabling FP8 precision for both weights and activations on Hopper GPUs. Weight-only quantization and FP8 precision are both build-time optimizations in TensorRT-LLM that reduce memory footprint and improve throughput without retraining. Weight-only quantization lowers weight precision, while FP8 leverages Hopper GPU capabilities for both weights and activations. The other options either require retraining or increase memory usage.

Answer

Pruning the model by removing entire attention heads

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Knowledge distillation from a larger teacher model

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Using a larger batch size to amortize memory overhead