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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q12-Q17):

NEW QUESTION # 12
A global financial institution is implementing an AI-driven fraud detection system that must process vast amounts of transaction data in real-time across multiple regions. The system needs to be highly scalable, maintain low latency, and ensure data security and compliance with various international regulations. The infrastructure should also support continuous model updates without disrupting the service. Which combination of NVIDIA technologies would best meet the requirements for this fraud detection system?

A. Use NVIDIA Jetson AGX Xavier devices for distributed data processing across regional offices.
B. Deploy the system on generic CPU-based servers with CUDA for accelerated computation.
C. Implement the system on NVIDIA Quadro GPUs with TensorFlow for model training and deployment.
D. Deploy the system on NVIDIA DGX A100 systems with NVIDIA Merlin for real-time data processing and model updates.

Answer: D

Explanation:
Deploying on NVIDIA DGX A100 systems with NVIDIA Merlin best meets the requirements for ascalable, low-latency, secure fraud detection system with continuous updates. DGX A100 provides high-performance GPU compute (e.g., 5 petaFLOPS AI performance) for real-time processing and training, while Merlin accelerates recommendation and fraud detection workflows with real-time feature engineering and model updates, ensuring minimal disruption. Option A (Quadro GPUs) lacks the scalability of DGX. Option C (CPU- based with CUDA) underutilizes GPU potential. Option D (Jetson AGX) suits edge, not centralized, processing. NVIDIA's financial use case documentation supports this combination.

NEW QUESTION # 13
Which industry has experienced the most profound transformation due to NVIDIA's AI infrastructure, particularly in reducing product design cycles and enabling more accurate predictivesimul-ations?

A. Automotive, by accelerating the development of autonomous vehicles and enhancing safety
B. Finance, by enabling real-time fraud detection and improving market predictions
C. Manufacturing, by automating quality control and improving supply chain logistics
D. Retail, by improving inventory management and enhancing personalized shopping experiences

Answer: A

Explanation:
The automotive industry (A) has seen the most profound transformation from NVIDIA's AI infrastructure.
NVIDIA's DRIVE platform and DGX systems accelerate autonomous vehicle development by reducing design cycles (e.g., via simulation with NVIDIA DRIVE Sim) and enabling accurate predictivesimul- ationsfor safety (e.g., sensor fusion, path planning). This has revolutionized prototyping and testing, cutting years off development timelines.
* Finance(B) benefits from real-time AI but focuses on transactions, not design cycles.
* Manufacturing(C) improves operations, but transformation is less tied to simulation-driven design.
* Retail(D) leverages AI for commerce, not product development.
NVIDIA's automotive AI leadership is well-documented (A).

NEW QUESTION # 14
In your AI data center, you are responsible for deploying and managing multiple machine learning models in production. To streamline this process, you decide to implement MLOps practices with a focus on job scheduling and orchestration. Which of the following strategies is most aligned with achieving reliable and efficient model deployment?

A. Deploy models directly to production without staging environments
B. Schedule all jobs to run at the same time to maximize GPU utilization
C. Use a CI/CD pipeline to automate model training, validation, and deployment
D. Manually trigger model deployments based on performance metrics

Answer: C

Explanation:
Using a CI/CD pipeline to automate model training, validation, and deployment (A) is the most aligned with reliable and efficient MLOps practices. Continuous Integration/Continuous Deployment (CI/CD) automates the ML lifecycle-building, testing, and deploying models-ensuring consistency, reducing errors, and enabling rapid iteration. Tools like Kubeflow or Jenkins, integrated with NVIDIA GPU Operator, schedule jobs efficiently on GPU clusters, validating models in staging environments before production rollout.
* Running all jobs simultaneously(B) risks resource contention and instability, not efficiency.
* Manual triggering(C) is slow and error-prone, counter to MLOps automation goals.
* Direct deployment without staging(D) skips validation, risking unreliable models in production.
NVIDIA supports CI/CD for AI deployment in its MLOps guidelines (A).

NEW QUESTION # 15
You are working on a project that involves monitoring the performance of an AI model deployed in production. The model's accuracy and latency metrics are being tracked over time. Your task, under the guidance of a senior engineer, is to create visualizations that help the team understand trends in these metrics and identify any potential issues. Which visualization would be most effective for showing trends in both accuracy and latency metrics over time?

A. Pie chart showing the distribution of accuracy metrics.
B. Dual-axis line chart with accuracy on one axis and latency on the other.
C. Stacked area chart showing cumulative accuracy and latency.
D. Box plot comparing accuracy and latency.

Answer: B

Explanation:
Tracking accuracy and latency trends over time requires a visualization that shows both metrics' evolution clearly. A dual-axis line chart, with accuracy on one axis and latency on the other, plots each as a line against time, revealing correlations (e.g., latency spikes reducing accuracy) and trends. NVIDIA RAPIDS supports such visualizations on GPUs, enhancing real-time monitoring in production environments like DGX or Triton deployments.
Pie charts (Option A) show distributions, not trends. Box plots (Option B) summarize static data, not time- based changes. Stacked area charts (Option C) imply cumulative values, confusing for independent metrics.
Dual-axis is NVIDIA-aligned for performance analysis.

NEW QUESTION # 16
In an MLOps pipeline, you are responsible for managing the training and deployment of machine learning models on a multi-node GPU cluster. The data used for training is updated frequently. How should you design your job scheduling process to ensure models are trained on the most recent data without causing unnecessary delays in deployment?

A. Train models only once per week and deploy them immediately after training.
B. Use a round-robin scheduling policy across all pipeline stages, regardless of data freshness.
C. Schedule the entire pipeline to run at fixed intervals, regardless of data updates.
D. Implement an event-driven scheduling system that triggers the pipeline whenever new data is available.

Answer: D

Explanation:
In an MLOps pipeline with frequently updated data, ensuring models are trained on the latest data without delaying deployment requires a responsive scheduling approach. An event-driven scheduling system, supported by tools like Kubernetes with NVIDIA GPU Operator or Apache Airflow integrated with NVIDIA GPUs, triggers the pipeline (data ingestion, training, and deployment) whenever new data arrives. This ensures freshness while minimizing idle time, aligning with NVIDIA's focus on efficient, automated AI workflows in production environments like DGX Cloud or NGC Catalog integrations.
Fixed intervals (Option A) risk training on outdated data or running unnecessarily when no updates occur.
Weekly training (Option B) introduces significant lag, unsuitable for frequent updates. Round-robin scheduling (Option D) lacks data-awareness, potentially misaligning resources and delaying critical updates.
Event-driven scheduling optimizes resource use and responsiveness, a key principle in NVIDIA's MLOps best practices.

NEW QUESTION # 17
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NVIDIA NCA-AIIO Exam | NCA-AIIO Exam Prep - Ensure you a High Passing Rate of NCA-AIIO Exam

NCA-AIIO Exam Reviews, Certification NCA-AIIO Test Questions

NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q12-Q17):

NCA-AIIO Exam Prep | Efficient NCA-AIIO Exam Reviews: NVIDIA-Certified Associate AI Infrastructure and Operations

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