FreeQAs
 Request Exam  Contact
  • Home
  • View All Exams
  • New QA's
  • Upload
PRACTICE EXAMS:
  • Oracle
  • Fortinet
  • Juniper
  • Microsoft
  • Cisco
  • Citrix
  • CompTIA
  • VMware
  • ISC
  • SAP
  • EMC
  • PMI
  • HP
  • Salesforce
  • Other
  • Oracle
    Oracle
  • Fortinet
    Fortinet
  • Juniper
    Juniper
  • Microsoft
    Microsoft
  • Cisco
    Cisco
  • Citrix
    Citrix
  • CompTIA
    CompTIA
  • VMware
    VMware
  • ISC
    ISC
  • SAP
    SAP
  • EMC
    EMC
  • PMI
    PMI
  • HP
    HP
  • Salesforce
    Salesforce
  1. Home
  2. NVIDIA Certification
  3. NCA-AIIO Exam
  4. NVIDIA.NCA-AIIO.v2026-09-18.q123 Dumps
  • ««
  • «
  • …
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • …
  • »
  • »»
Download Now

Question 31

Your team is building an AI-powered application that requires the deployment of multiple models, 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?

Correct Answer: D
NVIDIA Triton Inference Server is the best choice for deploying multiple models from different frameworks (TensorFlow, PyTorch, ONNX) in production. Triton provides a unified platform for serving models, supporting diverse frameworks with high performance on NVIDIA GPUs via features like dynamic batching and multi-model management. Option A (Clara Deploy SDK) is healthcare-specific. Option B (TensorRT) optimizes inference but isn't a full serving solution. Option C (DeepOps) aids deployment automation, not model serving. NVIDIA's Triton documentation emphasizes its versatility and efficiency for production inference across frameworks.
insert code

Question 32

You have developed two different machine learning models to predict house prices based on various features like location, size, and number of bedrooms. Model A uses a linear regression approach, while Model B uses a random forest algorithm. You need to compare the performance of these models to determine which one is better for deployment. Which two statistical performance metrics would be most appropriate to compare the accuracy and reliability of these models? (Select two)

Correct Answer: C,E
For regression tasks like predicting house prices (a continuous variable), the appropriate metrics focus on accuracy and reliability of numerical predictions:
* Mean Absolute Error (MAE)(C) measures the average absolute difference between predicted and actual values, providing a straightforward indicator of prediction accuracy. It's intuitive and effective for comparing regression models.
* R-squared (Coefficient of Determination)(E) indicates how well the model explains the variance in the target variable (house prices). A higher R-squared (closer to 1) suggests better fit and reliability, making it ideal for comparing Model A (linear regression) and Model B (random forest).
* F1 Score(A) is used for classification tasks, not regression, as it balances precision and recall.
* Learning Rate(B) is a hyperparameter for training, not a performance metric.
* Cross-Entropy Loss(D) is typically used for classification, not regression tasks like this.
MAE (C) and R-squared (E) are standard metrics in NVIDIA RAPIDS cuML and other ML frameworks for regression evaluation.
insert code

Question 33

Which metric is LEAST appropriate for evaluating recommendation ranking quality?

Correct Answer: D
Accuracy ignores ranking order and relevance, making it unsuitable for recommender systems.
insert code

Question 34

Which NVIDIA tool aids data center monitoring and management?

Correct Answer: D
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn't a standard NVIDIA tool, making DCGM the go-to solution.
insert code

Question 35

Which NVIDIA software provides the capability to virtualize a GPU?

Correct Answer: B
NVIDIA vGPU (Virtual GPU) software enables GPU virtualization by partitioning a physical GPU into multiple virtual instances, assignable to virtual machines or containers for accelerated workloads. Horizon is a VMware product, and "virtGPU" isn't an NVIDIA offering, confirming vGPU as the correct solution.
insert code
  • ««
  • «
  • …
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • …
  • »
  • »»
[×]

Download PDF File

Enter your email address to download NVIDIA.NCA-AIIO.v2026-09-18.q123 Dumps

Email:

FreeQAs

Our website provides the Largest and the most Latest vendors Certification Exam materials around the world.

Using dumps we provide to Pass the Exam, we has the Valid Dumps with passing guranteed just which you need.

  • DMCA
  • About
  • Contact Us
  • Privacy Policy
  • Terms & Conditions
©2026 FreeQAs

www.freeqas.com materials do not contain actual questions and answers from Cisco's certification exams.