Free NVIDIA NCA-AIIO Actual Exam Questions
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two)
A and B, because CUDA handles GPU computing and TensorRT optimizes AI models.
Probably A and B. CUDA Toolkit is basically the foundation for running any NVIDIA GPU compute tasks, so it’s a must-have in AI environments. TensorRT is super important too since it’s designed specifically for optimizing AI inference, which is a key part of deploying AI models efficiently. The others, like JetPack SDK, are more specialized for embedded systems, and Nsight Systems is more of a profiling tool rather than an essential component of the software stack itself. GameWorks is definitely not related to AI at all, so it can be ruled out easily.
being trained simultaneously on a shared GPU cluster. Some models require more GPU resources and
longer training times than others. Which orchestration strategy would best ensure that all models are
trained efficiently without causing delays for high-priority workloads?
Makes sense that without preemption, priority scheduling might not fully prevent delays. Still, random assignment (C) is clearly inefficient, so A remains the best option here by at least trying to allocate more resources to important jobs. A
A vs D? Giving equal resources (D) ignores different model needs, so some high-priority models might get delayed. A at least tries to match resources with priority, which should reduce overall wait times.
complex mathematical calculations and real-time data analytics. The calculations are CPU-intensive,
requiring precise sequential processing, while the data analytics involves processing large datasets in
parallel. How should you allocate the workloads across GPU and CPU architectures?
C/D? GPUs can speed up some math if it’s parallelizable, but the question says it’s precise and sequential, so that’s tricky. Data analytics usually benefits more from GPU’s parallel power, so C feels cleaner here.
Not B, CPUs aren’t just for I/O; they’re best for sequential math here.
and tracking changes in model experiments?
It’s A. Continuous Integration helps automate the tracking process by integrating code changes and tests frequently, which is key for managing experiment versions alongside the actual code changes.
B/D? Model Registry (B) is great for model versions, but Artifact Repository (D) can store all experiment files, including code and metrics, which might be better for full experiment tracking.
multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer
between the nodes to minimize latency. Which of the following networking technologies is most
suitable for this scenario?
C/D? While InfiniBand is great for low latency, 1 Gbps Ethernet might bottleneck heavy GPU data. Fiber Channel seems off since it's storage-focused, and Wi-Fi 6 just won’t cut it for this scale.
Guessing C, InfiniBand is built for high-performance GPU clusters, unlike others.
performance compared to running workloads on bare metal. Which factor is most likely contributing
to the performance degradation?
Probably B still makes the most sense here. Overcommitting GPU resources means the GPU scheduler has to juggle multiple workloads that all want heavy GPU time, which naturally slows down each one. The other options like networking or SSDs wouldn’t directly impact GPU compute speed. Even HA features mainly add resilience, not performance hits that big. Without proper passthrough or dedicated resources, the GPU just can’t keep up with multiple demanding workloads at once.
B, because sharing GPUs among too many VMs limits resources per VM.
After deployment, the company notices a significant delay in processing transactions, which impacts
their operations. Upon investigation, it’s discovered that the AI model is being heavily used during
peak business hours, leading to resource contention on the GPUs. What is the best approach to
address this issue?
D. If they have multiple GPUs or cloud instances, spreading the workload is the cleanest way to reduce delays caused by resource contention. Other options either slow processing or don’t really solve the core issue.
Makes sense to avoid A since CPUs are slower; that won’t solve the bottleneck. I’d say C isn’t ideal either because increasing batch size can add latency per transaction, which they want to avoid. So it’s really about handling the GPU resource problem. D is the only option that directly deals with spreading the load, assuming multiple GPUs or instances are available. So I agree with picking D here.
particularly in reducing product design cycles and enabling more accurate predictivesimul-ations?
It’s A. Automotive’s use of AI for autonomous vehicle development is a huge leap, speeding up design and safety testing way more than the others, which are more about optimization than complete transformation.
A/C? Automotive also speeds up design cycles with AI, especially for autonomous tech, but manufacturing’s broader use of simulations for quality and logistics seems a bit more on point here.
infrastructure is intended to support multiple AI workloads, including training, inference, and
dataanalysis. You have been tasked with analyzing system logs to identify performance bottlenecks
under the supervision of a senior engineer. Which log file would be most useful to analyze when
diagnosing GPU performance issues in this scenario?
Guessing B here, since nvidia-smi logs give real-time GPU utilization and memory stats, which are key for spotting when the GPU is maxed out or idle. Kernel logs might be too generic for performance specifics.
It’s C because kernel logs capture driver or hardware faults that nvidia-smi won’t show, which can seriously impact GPU performance even if utilization looks fine. This helps spot hidden issues beyond just load stats.
requires both data preprocessing and training across multiple GPUs. They need to ensure that the
GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies
should they use?
B tbh, DALI and NCCL in option C do sound solid for preprocessing and multi-GPU syncing, but just to add—DeepStream SDK (part of B) is more for video analytics, so that’s probably not relevant here. CUDA Toolkit, however, is the base for GPU programming, so it’s essential. Since the question emphasizes data preprocessing plus multi-GPU training efficiency, the combo in C really fits best. The others either focus on inference optimization or specific OS/catalog stuff, which doesn’t directly tackle the data loading and communication challenge this NLP team faces.
It’s C because DALI is great for speeding up data loading and preprocessing, while NCCL is built specifically for efficient GPU communication, which is crucial when training across multiple GPUs. Other options don’t handle both parts as well.
each trained using different frameworks (e.g., TensorFlow, PyTorch, and ONNX). You need a
deployment solution that can efficiently serve all these models in production, regardless of the
framework they were built in. Which software component should you choose?
Maybe D – it’s designed to serve models from various frameworks without extra conversion steps, unlike B which mainly focuses on optimization rather than serving multiple frameworks directly.
B imo, TensorRT optimizes models from different frameworks for faster inference.
expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is
low, and the CPU utilization is very high. What is the most likely cause of this issue?
Yeah, I’m with D too. If the CPUs are maxed out, the GPUs end up waiting for data instead of training. Adding GPUs won’t help if the bottleneck is before the data even reaches them. It’s basically a pipeline issue—preprocessing or loading needs to be sped up or parallelized to see any GPU utilization gains.
Maybe D, since high CPU and low GPU usage often means the GPUs are waiting on data. If the CPUs are busy preprocessing, that would explain the slowdown despite adding GPUs.
workloads, allowing data scientists to build and deploy models at scale using GPUs?
C makes more sense since RAPIDS focuses on data science workflows, not just hardware like DGX.
C imo since RAPIDS is built specifically for data analytics and ML acceleration on GPUs, making it ideal for scaling model development and deployment. DGX A100 is hardware, but the question focuses on the solution for analytics workloads.
and performing complex matrix operations. The team is debating whether to use GPUs or CPUs to
achieve the best performance. What is the most compelling reason to choose GPUs over CPUs for
this specific use case?
The parallel processing strength of GPUs is a big deal here, so definitely B.
It’s B for me. The key is that GPUs handle thousands of threads at once, making them way better for matrix-heavy AI tasks than CPUs, which are more about fewer, faster cores. Options A and C focus on cache and single-thread speed, but those don’t really match the workload here. As for D, energy efficiency is nice but not the priority when you need raw performance to crunch big data fast. Even if dataset size is a concern, the question asks for the most compelling reason, and that’s definitely the parallel processing power of GPUs.
experiencing performance degradation. Which GPU monitoring metric is most critical for identifying
resource contention between jobs?
Good points on A showing compute contention, but what if the compute units aren’t fully used yet performance still drops? Could D be more telling since memory bandwidth throttling can cause hidden slowdowns even with moderate GPU utilization?
Maybe A is better here since high GPU utilization shows if the compute cores are actually competing for resources. Memory bandwidth matters but might not show direct contention like utilization does.