NCP-AIO Practice Test 21 — 15 Questions

Question 1

An AI researcher is using Nsight Systems to profile an application. They notice a large gap in the timeline where neither the CPU nor the GPU is performing significant work. What does this gap most likely represent?

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

Accepted Answer

The application is performing blocking synchronization.. Large gaps in profiling timelines often indicate synchronization points where the CPU is waiting for the GPU to finish a task, or vice versa, due to blocking operations. These 'stalls' are common in poorly optimized code that issues many synchronous calls. Identifying these gaps allows the developer to re-structure the code to use asynchronous execution, thereby overlapping compute and data movement to improve performance.

Answer

The GPU is busy performing background garbage collection.

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The profiler has encountered a buffer overflow.

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The system is entering a power-saving state.

Question 2

An administrator is setting up NVIDIA Base Command Manager to provision and manage a new AI cluster. They need to ensure that the cluster can automatically discover and configure new GPU nodes. Which component is responsible for node discovery and initial configuration?

Accepted Answer

NVIDIA Base Command Manager head node. In NVIDIA Base Command Manager, the head node is the central management server that performs node discovery and initial configuration. It provides the provisioning services that allow new GPU nodes to be automatically detected and configured. The GPU Operator, Fleet Command, and Morpheus serve different purposes and are not involved in the initial provisioning of a Base Command Manager cluster.

Answer

NVIDIA Fleet Command

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NVIDIA GPU Operator

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NVIDIA Morpheus

Question 3

What is the most accurate way to verify that a training job is utilizing Tensor Cores?

Accepted Answer

Monitor the SM Tensor utilization metric via Nsight Compute.. Tensor Cores are specialized hardware units for mixed-precision matrix operations. Monitoring the SM occupancy and specific instruction usage via Nsight Compute is the definitive way to confirm their engagement. Understanding how to verify this is essential for engineers to ensure that their optimization efforts, such as mixed-precision training, are actually resulting in the intended hardware utilization and performance improvements.

Answer

Check the GPU power consumption in nvidia-smi.

Answer

Check the memory bandwidth in DCGM.

Answer

Verify the CUDA driver version is above 500.0.

Question 4

When installing the NVIDIA Container Toolkit to enable GPU acceleration in Docker, which file must be modified or verified to ensure the container runtime can access the NVIDIA runtime?

Accepted Answer

/etc/docker/daemon.json. The daemon configuration file, typically located at /etc/docker/daemon.json, must be updated to include the NVIDIA runtime. This integration is the foundational step for AI containerization, as it allows the Docker engine to map host GPU resources into the container namespace. Without this configuration, containers will fail to detect GPUs, rendering them incapable of running accelerated AI applications.

Answer

/etc/fstab

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/etc/environment

Answer

/boot/grub/grub.cfg

Question 5

A systems administrator is installing the NVIDIA Container Toolkit on a stand-alone server running Ubuntu 22.04 to enable Docker containers to access NVIDIA GPUs. After installation, they run a test container and find that it cannot see the GPU. Which step is most likely missing?

Accepted Answer

Configuring the Docker daemon to use the NVIDIA container runtime as the default runtime.. After installing the NVIDIA Container Toolkit, the Docker daemon must be configured to use the NVIDIA container runtime. This involves editing '/etc/docker/daemon.json' to add the runtime and restarting Docker. Without this configuration, containers will not have GPU access, even though the toolkit is installed.

Answer

Rebooting the server after installing the toolkit.

Answer

Adding the user to the 'docker' group to run containers with GPU support.

Answer

Installing the NVIDIA GPU driver on the host.

Question 6

An AI operations engineer is deploying a model on an NVIDIA GPU and notices that inference latency is higher than expected. The model uses a batch size of 1, and profiling shows that the GPU is idle between kernel launches. Which optimization technique should the engineer use to reduce latency by overlapping data transfer with computation?

Accepted Answer

Use CUDA streams to overlap host-to-device memory copies with kernel execution.. The idle time between kernel launches indicates that data transfers and kernel executions are serialized. Using CUDA streams to overlap host-to-device copies with kernel execution can hide transfer latency and reduce overall inference time. This is the correct technique for overlapping data transfer with computation, especially with small batch sizes where transfer overhead is significant.

Answer

Enable NVIDIA MPS to allow multiple processes to share the GPU.

Answer

Switch to a lower-precision data type such as FP16 to reduce memory transfer size.

Answer

Increase the batch size to improve GPU utilization and reduce per-inference latency.

Question 7

Which TWO of the following actions should be taken to optimize GPU memory usage when encountering Out-of-Memory (OOM) errors during model training?

Accepted Answer

Implement gradient checkpointing to trade compute for memory.. OOM errors are common in deep learning when the model size, activation memory, and batch size exceed VRAM capacity. Implementing gradient checkpointing and mixed-precision training are standard industry practices to manage memory pressure. Mastery of these techniques is essential for AI operations engineers to maintain system stability and enable the training of large-scale models without requiring immediate hardware upgrades.

Answer

Increase the number of CPU threads in the data loader.

Answer

Disable the use of NCCL to reduce inter-node memory overhead.

Answer

Switch from the Adam optimizer to Stochastic Gradient Descent (SGD).

Question 8

A team is deploying a large language model for inference using NVIDIA Triton Inference Server on a GPU. They observe that the first inference request has high latency compared to subsequent requests. What is the most likely cause and the appropriate optimization?

Accepted Answer

The first request triggers model loading and CUDA context initialization; enable model warmup in Triton to pre-load and initialize the model.. The first inference request often incurs overhead from loading the model into GPU memory, compiling kernels, and initializing CUDA contexts. Triton's model warmup feature performs dummy inferences at startup, effectively pre-warming the model. This shifts the initialization cost away from the first real request, resulting in consistent low latency for all requests.

Answer

The GPU is thermal throttling on the first request; set a higher power limit using nvidia-smi -pl to stabilize performance.

Answer

The model is not using TensorRT; converting it to a TensorRT engine will eliminate first-request latency.

Answer

The model uses dynamic batching; disable dynamic batching to ensure the first request is processed immediately.

Question 9

An AI operations team is troubleshooting a distributed training job on an NVIDIA DGX SuperPOD that uses NCCL for inter-GPU communication. The job intermittently hangs during the all-reduce phase. Which two actions should be taken to diagnose and resolve the issue? (Choose two.)

Accepted Answer

Set NCCL_DEBUG=INFO and NCCL_DEBUG_SUBSYS=INIT,COLL to capture detailed NCCL initialization and collective logs.. Intermittent hangs in NCCL all-reduce often stem from configuration or network issues. Enabling detailed NCCL logging helps identify the exact failure point, while verifying consistent NCCL versions and network interface health addresses common root causes. These two actions together provide both diagnostic information and a path to resolution without degrading performance.

Answer

Disable the use of InfiniBand and force NCCL to use TCP sockets for communication.

Answer

Restart the training job with a smaller number of GPUs to see if the hang persists.

Answer

Increase the batch size to reduce the frequency of all-reduce operations.

Question 10

Which TWO of the following are prerequisites for installing the NVIDIA Container Toolkit on a Linux host?

Accepted Answer

A compatible NVIDIA driver already installed on the host.. To successfully deploy the NVIDIA Container Toolkit, the host must have a functional NVIDIA driver and a container runtime like Docker or containerd. These prerequisites ensure that the runtime has a target to interface with and can correctly map host-side GPU resources into the container namespace. Without these core components, the toolkit cannot bridge the gap between physical hardware and isolated containerized processes.

Answer

The latest version of the CUDA Toolkit installed in every container.

Answer

An active subscription to NVIDIA AI Enterprise.

Answer

A pre-configured Kubernetes cluster with Helm.

Question 11

Which configuration file is typically modified to enable the NVIDIA Device Plugin in a Kubernetes cluster?

Accepted Answer

A Kubernetes DaemonSet manifest (YAML).. The NVIDIA Device Plugin is typically deployed as a DaemonSet using a YAML manifest. Modifying this manifest allows administrators to customize settings like the MIG strategy, time-slicing, and resource allocation policies. Correct configuration of this file is essential for ensuring that the Kubernetes scheduler correctly recognizes and assigns GPU resources to pods, which is the cornerstone of effective AI cluster management in modern cloud-native environments.

Answer

/etc/docker/daemon.json

Answer

/etc/nvidia/nvidia-container-runtime.json

Answer

/etc/kubernetes/kubelet.conf

Question 12

An AI operations engineer is validating a new NVIDIA-certified server before putting it into production. The job runs correctly but the team wants to confirm that the GPUs are operating at the expected clocks and not being limited by power or thermal constraints. Which command provides the most direct evidence of the current power and thermal limits and any throttling reasons?

Accepted Answer

`nvidia-smi -q -d PERFORMANCE`. The performance query reports clocks, performance state, and explicit throttle reason flags such as power cap, thermal slowdown, and hardware limit. That combination gives direct evidence of whether a GPU is constrained and by what. Other commands either report inventory or topology and cannot answer the throttling question.

Answer

`nvidia-smi --query-gpu=name,driver_version --format=csv`

Answer

`nvidia-smi topo -m`

Answer

`nvidia-smi --gpu-reset -i 0`

Question 13

When managing workloads on NVIDIA DGX systems, what is the primary role of the NVIDIA device plugin in the Kubernetes ecosystem?

Accepted Answer

To monitor the health status of GPUs and advertise them to the Kubernetes scheduler.. The NVIDIA device plugin acts as an interface between Kubernetes and the NVIDIA drivers, enabling the scheduler to advertise and allocate GPUs as first-class resources. It is fundamental to GPU workload management because it informs the kubelet about the availability, health, and count of GPUs on the node, ensuring that pods requesting GPUs are placed on nodes with sufficient capacity.

Answer

To compile CUDA code for specific GPU architectures during the job scheduling process.

Answer

To automatically optimize neural network hyper-parameters for faster training throughput.

Answer

To replace the need for the NVIDIA Container Toolkit in the container runtime environment.

Question 14

Which file format is commonly used to define the configuration and state for the NVIDIA GPU Operator within a Kubernetes environment?

Accepted Answer

YAML. Kubernetes uses YAML to define declarative states for resources, including Custom Resource Definitions (CRDs) used by operators. The GPU Operator leverages YAML manifests to configure settings such as driver version, toolkit installation, and monitoring agents. Understanding this format is vital for AI Ops professionals, as it allows for version-controlled infrastructure-as-code practices, ensuring deployments are reproducible and auditable across various environments.

Answer

JSON

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TOML

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XML

Question 15

When deploying NVIDIA AI Enterprise, why is the use of the NVIDIA NGC Catalog recommended over public container repositories?

Accepted Answer

NGC images are curated and optimized for NVIDIA hardware compatibility.. The NGC Catalog provides verified, performance-optimized, and security-scanned container images specifically tailored for the NVIDIA AI Enterprise stack. These images contain all necessary dependencies and are guaranteed to be compatible with supported GPU drivers. Using these images reduces the risk of deployment failures caused by library version mismatches, missing dependencies, or unoptimized software versions that are common in generic, public repositories, thereby ensuring a reliable production-grade AI environment.

Answer

NGC images are the only way to bypass the need for a valid NVIDIA license.

Answer

NGC images are pre-configured to automatically perform hardware diagnostics.

Answer

NGC images can be deployed without any need for the NVIDIA Container Toolkit.