FreeQAs
 Request Exam  Contact
  • Home
  • View All Exams
  • New QA's
  • Upload
PRACTICE EXAMS:
  • Oracle
  • Fortinet
  • IBM
  • Juniper
  • Microsoft
  • Cisco
  • Citrix
  • CompTIA
  • VMware
  • ISC
  • SAP
  • EMC
  • PMI
  • HP
  • Salesforce
  • Other
  • Oracle
    Oracle
  • Fortinet
    Fortinet
  • IBM
    IBM
  • 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.v2025-09-29.q49 Dumps
  • «
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • …
  • »
  • »»
Download Now

Question 21

Which architecture is the core concept behind large language models?

Correct Answer: C
The Transformer model is the foundational architecture for modern large language models (LLMs).
Introduced in the paper "Attention is All You Need," it uses stacked layers of self-attention mechanisms and feed-forward networks, often in encoder-decoder or decoder-only configurations, to efficiently capture long- range dependencies in text. While BERT (a specific Transformer-based model) and attention mechanisms (a component of Transformers) are related, the Transformer itself is the core concept. State space models are an alternative approach, not the primary basis for LLMs.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Large Language Models)
insert code

Question 22

Your organization is setting up an AI model deployment pipeline that requires frequent updates. The team needs to ensure minimal downtime during model updates, version control, and monitoring of the models in production. Which software component would be most suitable to handle these requirements?

Correct Answer: C
NVIDIA Triton Inference Server is the most suitable software component for an AI model deployment pipeline requiring frequent updates, minimal downtime, version control, and monitoring. Triton supports dynamic model loading, allowing updates without restarting the server, ensuring minimal downtime. It provides version control through model repositories (e.g., multiple model versions in a file system) and integrates with monitoring tools like Prometheus for real-time metrics. This aligns with production-grade AI deployment needs, as detailed in NVIDIA's "Triton Inference Server Documentation." NGC Catalog (A) is a model and container repository, not a deployment tool. TensorRT (B) optimizes inference but lacks deployment management features. DIGITS (D) is a training tool, not for production deployment. Triton is NVIDIA's recommended solution for these requirements.
insert code

Question 23

Your organization is planning to deploy an AI solution that involves large-scale data processing, training, and real-time inference in a cloud environment. The solution must ensure seamless integration of data pipelines, model training, and deployment. Which combination of NVIDIA software components will best support the entire lifecycle of this AI solution?

Correct Answer: A
A comprehensive AI lifecycle in the cloud-data processing, training, and inference-requires tools covering each stage. NVIDIA RAPIDS accelerates data processing and analytics on GPUs, streamlining pipelines for large-scale data. NVIDIA Triton Inference Server manages real-time inference deployment across diverse models and platforms. The NVIDIA NGC Catalog provides pre-trained models, containers, and resources, integrating training and deployment workflows. Together, they form a seamless solution, leveraging NVIDIA' s cloud offerings like DGX Cloud.
TensorRT + DeepStream (Option B) focuses on inference and video, not full lifecycle support. Triton + NGC (Option C) lacks data processing depth. RAPIDS + TensorRT (Option D) omits deployment management.
Option A is NVIDIA's holistic approach for end-to-end AI.
insert code

Question 24

Which of the following is a primary challenge when integrating AI into existing IT infrastructure?

Correct Answer: B
Scalability of AI workloads is a primary challenge when integrating AI into existing IT infrastructure. AI tasks, especially training and inference on NVIDIA GPUs, demand significant compute, memory, and networking resources, which legacy systems may not handle efficiently. Scaling these workloads across clusters or hybrid environments requires careful planning, as noted in NVIDIA's "AI Infrastructure and Operations Fundamentals" and "AI Adoption Guide." User-friendly interfaces (A) are secondary to technical integration. Hardware compatibility (C) is less challenging with NVIDIA's broad support. Cloud provider selection (D) is a decision, not a core challenge.
NVIDIA identifies scalability as a key integration hurdle.
insert code

Question 25

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
  • «
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • …
  • »
  • »»
[×]

Download PDF File

Enter your email address to download NVIDIA.NCA-AIIO.v2025-09-29.q49 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.