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[General] Test AT-510 Result | AT-510 Exam Tutorial

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【General】 Test AT-510 Result | AT-510 Exam Tutorial

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AI CERTs AI+ NetworkExamination Sample Questions (Q37-Q42):NEW QUESTION # 37
(Scenario: A multinational corporation with offices in multiple countries is experiencing significant delays in data processing due to the centralized routing of all traffic to a single data center. The company wants to minimize latency and improve real-time processing capabilities while ensuring that data remains secure within the local regions.
Question: What strategy should they adopt to address these challenges?)
  • A. Implement edge computing to process data closer to its origin and reduce latency.
  • B. Consolidate traffic into fewer subnets using VLANs for simplicity.
  • C. Deploy a single VNET to handle all global traffic centrally.
  • D. Use a hybrid cloud setup to distribute traffic across multiple public and private networks.
Answer: A
Explanation:
Implementing edge computing is the most effective strategy to reduce latency and enhance real-time data processing in geographically distributed environments. AI+ Network documentation highlights edge computing as a modern architectural approach where data is processed closer to its source rather than being sent to a centralized data center. This significantly reduces transmission delays, which is critical for real-time analytics, collaboration tools, and latency-sensitive applications.
For multinational organizations, edge computing enablesregional data locality, ensuring that sensitive data remains within local jurisdictions, supporting regulatory compliance and security requirements. By processing data at or near regional offices, the organization reduces reliance on long-haul WAN links, minimizing congestion and improving application responsiveness.
Options such as centralized VNETs or VLAN consolidation do not address latency issues and may worsen bottlenecks. While hybrid cloud improves flexibility, it does not inherently solve real-time processing delays unless paired with edge capabilities. AI+ Network trends clearly identify edge computing as a foundational technology for distributed enterprises seeking performance, resilience, and compliance.

NEW QUESTION # 38
(Scenario: A company needs a network design that maintains high performance while ensuring reliability.
Question: Which combination of strategies would best achieve this?)
  • A. Load balancing with redundant connections.
  • B. Star topology with failover systems.
  • C. Cloud infrastructure with fault tolerance.
  • D. Centralized routing with hybrid topology.
Answer: A
Explanation:
Load balancing combined with redundant connections is the most effective strategy for achieving both high performance and reliability in modern network designs. According to AI+ Networkfoundational principles, load balancingdistributes traffic evenly across multiple network paths, links, or devices, preventing congestion and ensuring optimal resource utilization. This directly improves performance by avoiding single points of saturation.
Redundant connectionscomplement load balancing by providing alternate paths in case of link, device, or circuit failure. If one connection becomes unavailable, traffic is automatically rerouted through another active path, maintaining service continuity without noticeable downtime. AI+ Network documentation emphasizes redundancy as a critical design principle for high-availability architectures, particularly in enterprise and mission-critical environments.
While star topology with failover improves reliability, it can still suffer from central bottlenecks. Centralized routing introduces single points of failure, and cloud fault tolerance alone does not address on-premise or hybrid network performance challenges. In contrast, load balancing with redundancy directly addresses both throughput optimizationandfault toleranceat the network layer.
Therefore, this combination best satisfies the requirement of maintaining high performance while ensuring consistent and reliable network operations.

NEW QUESTION # 39
(How do AI frameworks simplify model development for networking solutions?)
  • A. By limiting model designs to a single use case.
  • B. By focusing only on manual coding for each specific model.
  • C. By requiring advanced expertise in deep learning for all implementations.
  • D. By providing pre-built algorithms to abstract low-level details.
Answer: D
Explanation:
AI frameworks simplify model development for networking solutions by providing pre-built algorithms and abstractions that hide low-level implementation complexity. According to AI+ Network documentation, frameworks such as TensorFlow, PyTorch, and specialized networking AI libraries enable engineers to focus on problem-solving rather than mathematical and architectural details.
These frameworks include optimized libraries for data processing, training, validation, and deployment, significantly reducing development time. In networking use cases-such as traffic prediction, anomaly detection, and performance optimization-pre-built models can be adapted quickly without designing algorithms from scratch.
Contrary to requiring advanced deep learning expertise, AI frameworks lower the entry barrier for network engineers by offering modular components and reusable templates. They also support scalability and integration with automation platforms, aligning with AI+ Network goals of agility and efficiency.
Limiting models to a single use case or relying solely on manual coding contradicts the purpose of frameworks. AI+ Network materials clearly position AI frameworks as accelerators for innovation in intelligent networking solutions.

NEW QUESTION # 40
(Scenario: A multinational corporation faces an issue where employees working remotely often connect to corporate resources using unsecured devices. Despite enforcing strong password policies, they still encounter breaches due to compromised endpoints. The security team needs a strategy to ensure only compliant devices can access sensitive resources while minimizing user disruption.
Question: What approach should the corporation adopt to resolve this issue?)
  • A. Enforce stricter password policies to enhance user authentication security.
  • B. Restrict remote access entirely to prevent breaches from unsecured devices.
  • C. Deploy network segmentation to isolate critical resources from remote access.
  • D. Implement Zero Trust Architecture to verify user and device compliance.
Answer: D
Explanation:
Implementing a Zero Trust Architecture (ZTA) is the most effective approach for securing access from remote and potentially unsecured devices. AI+ Network security documentation explains that Zero Trust operates on the principle of "never trust, always verify," requiring continuous validation of both user identity and device posture before granting access.
Unlike traditional perimeter-based security, Zero Trust evaluates device compliance factors such as operating system health, patch status, and endpoint security controls. Access is granted dynamically and contextually, minimizing disruption while significantly reducing risk. Even authenticated users are restricted to least- privilege access.
Stricter passwords alone do not address compromised endpoints, and completely restricting remote access harms productivity. Network segmentation helps limit damage but does not verify endpoint integrity. AI+ Network frameworks clearly identify Zero Trust as the preferred model for modern, distributed workforces.

NEW QUESTION # 41
(What is a key advantage of using Ansible for network automation?)
  • A. It limits network management to Linux-based devices only.
  • B. It relies on Ruby scripts for configuration tasks.
  • C. It mandates pre-installation of agents on managed devices.
  • D. It utilizes an agentless architecture for managing devices.
Answer: D
Explanation:
Ansible's key advantage in network automation is itsagentless architecture, which allows devices to be managed without installing additional software on them. AI+ Network automation documentation emphasizes that Ansible uses standard protocols such as SSH and APIs to communicate with network devices, making deployment simple and scalable.
This design significantly reduces operational overhead and security risks associated with maintaining agents across hundreds or thousands of devices. Ansible playbooks, written in YAML, define desired configurations in a clear, human-readable format, improving collaboration and reducing configuration errors.
Unlike Chef, which relies on Ruby-based cookbooks, Ansible does not require specialized programming knowledge. It also supports a wide range of vendors and platforms beyond Linux. AI+ Network materials consistently position Ansible as an efficient, low-complexity automation tool ideal for both enterprise and multi-vendor network environments.

NEW QUESTION # 42
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