NCP-AIO Practice Test 3 — 10 Questions

Question 1

A production inference service using TensorRT is showing lower than expected throughput. Profiling shows that the model is spending significant time in "host-to-device" transfers. What is the best optimization?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use pinned memory for data transfers.. Data movement is a primary latency bottleneck in deep learning inference. By using pinned memory (page-locked memory) for host-side buffers, the system can enable faster direct memory access (DMA) transfers between the CPU and the GPU. This minimizes the time spent in data copy operations, directly reducing latency and increasing total throughput, which is essential for meeting strict Service Level Agreements (SLAs) in production AI deployments.

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Increase the GPU clock speed via nvidia-smi.

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Switch to FP64 precision for higher accuracy.

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Implement multi-threaded data preprocessing on the GPU.

Question 2

Which TWO of the following are benefits of using containerized GPU workloads compared to bare-metal deployment?

Accepted Answer

Easier management of heterogeneous software dependencies.. Containerization provides portability, version control, and dependency isolation, which are essential for managing complex AI stacks. By packaging the environment, developers ensure that models run consistently across development, testing, and production clusters. This significantly reduces 'environment drift' and simplifies the management of different CUDA, cuDNN, and framework versions required by various research projects within the same shared infrastructure cluster.

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Significant increase in raw GPU compute performance.

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Automatic elimination of GPU driver compatibility issues.

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Direct access to the GPU firmware for kernel customization.

Question 3

When managing large-scale model training jobs, what is the primary purpose of using a Job Scheduler like Slurm or Kubernetes Batch?

Accepted Answer

To automate resource allocation, job queuing, and throughput optimization.. Job schedulers serve as the orchestration layer to queue, prioritize, and allocate computational resources based on policy. In AI operations, they ensure that high-priority training runs receive the necessary GPU throughput while lower-priority jobs wait. By automating the allocation process, schedulers prevent resource idleness and manage contention, allowing data scientists to focus on model development rather than manual infrastructure management or resource conflict resolution during heavy cluster usage.

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To increase the clock speed of individual GPU cores.

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To provide a direct IDE interface for writing model code.

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To replace the need for containerization technologies.

Question 4

An engineer is troubleshooting a CUDA program that terminates unexpectedly. Which tool should be used to detect memory leaks and race conditions in the CUDA kernel code?

Accepted Answer

NVIDIA Compute Sanitizer. Debugging parallel code is inherently difficult due to the non-deterministic nature of race conditions and memory access patterns. The NVIDIA Compute Sanitizer is the essential diagnostic tool for identifying these issues, such as out-of-bounds memory access or race conditions, before they lead to erratic, intermittent crashes in production. Using it early in the development lifecycle saves massive amounts of time by ensuring code correctness and robustness.

Answer

nvidia-smi

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nsys (Nsight Systems)

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nvprof

Question 5

Which security configuration is necessary when deploying NVIDIA GPUs in a multi-tenant environment to prevent unauthorized access between containers?

Accepted Answer

Enable MIG (Multi-Instance GPU).. In multi-tenant environments, using MIG (Multi-Instance GPU) is the standard method for hardware-level isolation. MIG allows a single physical GPU to be partitioned into multiple isolated instances, each with its own dedicated compute and memory. This ensures that one tenant's workload cannot interfere with or access the resources of another, providing a robust security boundary that software-only isolation cannot reliably guarantee in accelerated computing environments.

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Increase the Kubernetes pod memory limit.

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Use a privileged container.

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Install the latest version of CUDA.

Question 6

What is the primary function of an 'InitContainer' in an NVIDIA GPU-enabled pod deployment?

Accepted Answer

To ensure prerequisites are satisfied before the main application starts.. InitContainers are often used to ensure that environment-specific requirements—such as configuring GPU drivers, validating libraries, or mounting persistent volumes—are met before the main training application starts. In AI workloads, this is critical for ensuring that dependencies are correctly loaded or data is staged, preventing runtime failures that would occur if the main training process attempted to execute in an incomplete environment. This pattern enhances the robustness of automated AI workflows.

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To serve as a secondary GPU compute thread during training.

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To permanently store the model weights after training.

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To bypass GPU scheduling constraints and force-load the job.

Question 7

An AI operations engineer is troubleshooting an inference service on an NVIDIA A100 GPU that shows intermittent stalls. The monitoring dashboard reports GPU utilization at 100%, but request throughput is far below the validated baseline. Running nvidia-smi dmon reveals the SM utilization is high while memory controller utilization is low. Which action should the engineer take first to identify the bottleneck?

Accepted Answer

Profile the inference process with Nsight Systems to inspect kernel execution and identify serialization gaps.. The high SM utilization with low memory controller utilization suggests the GPU is busy but not doing useful work, often due to kernel serialization or CPU-GPU synchronization stalls. Profiling with Nsight Systems provides the timeline needed to see gaps and serialization. Only after identifying the specific stall should configuration changes be considered, making profiling the correct first step.

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Switch the inference precision from FP16 to FP32 to improve numerical stability.

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Enable MPS (Multi-Process Service) to allow concurrent kernel execution from multiple processes.

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Increase the batch size in the inference server configuration to improve GPU occupancy.

Question 8

What is the primary function of the NVIDIA Persistence Daemon in an AI deployment?

Accepted Answer

It keeps the GPU driver initialized to reduce latency during application startup.. The Persistence Daemon ensures that the NVIDIA driver remains loaded even when no applications are using the GPU. This prevents the driver from unloading and then reloading when a job starts, which significantly reduces the startup latency of AI models. It is a critical configuration for high-performance environments where frequent job scheduling would otherwise incur unnecessary overhead from repeated driver and device initialization.

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It automatically updates the NVIDIA driver when a new version is released on the web.

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It monitors GPU health and automatically initiates a reboot if an error is detected.

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It manages the network traffic between the GPU and the storage backend.

Question 9

A financial services firm must prove to auditors that an AI training job ran on hardware located only in its Frankfurt data center and that no pod could ever be scheduled onto GPUs in other regions. The cluster spans three regions with nodes labeled topology.kubernetes.io/region. Which approach most directly enforces this placement requirement?

Accepted Answer

Define a nodeAffinity rule in the pod spec requiring topology.kubernetes.io/region to equal eu-central-1, and mark it requiredDuringSchedulingIgnoredDuringExecution.. Hard placement guarantees come from required node affinity, which makes matching labels a precondition for scheduling. Because the scheduler will not place the pod on any node whose region label differs, the job cannot leave the Frankfurt data center, giving the firm an enforceable, auditable control over GPU location.

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Add a preferredDuringSchedulingIgnoredDuringExecution nodeAffinity rule that favors the Frankfurt region with a high weight.

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Set the pod's restartPolicy to Never and add a toleration for the region-specific taint on Frankfurt nodes.

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Create a PodDisruptionBudget for the training job that limits voluntary evictions to zero during the run.

Question 10

An administrator needs to ensure that all GPU drivers are updated across a heterogeneous cluster without causing downtime. What is the best strategy?

Accepted Answer

Perform rolling updates by draining nodes one by one. Implementing a rolling update strategy allows the cluster to be updated one node at a time while the remaining nodes continue to process training jobs. By draining the target node of all active jobs, upgrading the drivers, and then re-adding the node to the production pool, the administrator maintains service availability. This is the standard practice in production AI operations to minimize disruption and ensure consistent driver versions across the entire fleet.

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Simultaneously update all nodes during a maintenance window

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Use a containerized driver approach to avoid host updates

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Only update the driver on the master node