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Get Excellent Scores in Exam with AI CERTs AT-510 Questions
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AI CERTs AI+ NetworkExamination Sample Questions (Q38-Q43):NEW QUESTION # 38
(What distinguishes Kubernetes in the orchestration of containerized applications?)
- A. It uses YAML files for device-level configuration tasks.
- B. It restricts workloads to a single server for improved performance.
- C. It requires manual intervention to balance workloads across nodes.
- D. It automates deployment and scaling while managing container lifecycles.
Answer: D
Explanation:
Kubernetes is distinguished by its ability to fully automate the deployment, scaling, and lifecycle management of containerized applications. According to AI+ Network advanced networking documentation, Kubernetes operates as acontainer orchestration platformthat abstracts infrastructure complexity and ensures applications remain available, scalable, and resilient.
Kubernetes continuously monitors the state of containers and nodes, automatically restarting failed containers, rescheduling workloads when nodes go down, and scaling applications up or down based on demand. This self-healing and auto-scaling capability eliminates the need for manual workload balancing, which is a major advantage in dynamic, cloud-native environments.
While Kubernetes does use YAML files, these are not for device-level configurations but for declarative application definitions. It also supports distributed workloads across multiple nodes and clusters, rather than restricting applications to a single server. AI+ Network materials emphasize Kubernetes as a foundational technology for microservices, multi-cloud deployments, and AI-driven infrastructure due to its automation- first design.
NEW QUESTION # 39
(Scenario: A financial services company is experiencing an unusual number of login attempts from different global IP addresses on an employee account. They need to determine whether the account is compromised while ensuring minimum disruption to operations.
Question: Which AI-driven security feature would best address this issue?)
- A. Behavioral analysis to compare current activity with the account's baseline patterns.
- B. Heuristic analysis to apply generalized rules for identifying threats.
- C. Static analysis to evaluate metadata associated with the login attempts.
- D. Signature-based detection to match activity with known threat databases.
Answer: A
Explanation:
Behavioral analysis is the most effective AI-driven security feature for detecting potential account compromise while minimizing operational disruption. AI+ Network security frameworks emphasize behavioral analysis as a technique that establishes abaseline of normal user behavior, including login locations, times, devices, and access patterns.
When deviations occur-such as simultaneous or rapid login attempts from multiple global IP addresses-the AI system flags the activity as anomalous without immediately blocking access. This allows security teams to investigate potential compromise while maintaining business continuity. Unlike signature-based detection, which only identifies known threats, behavioral analysis can detectpreviously unseen or zero-day attack patterns.
Static and heuristic analyses are less precise in this context, as they rely on predefined rules or metadata rather than adaptive learning. Financial institutions, in particular, benefit from behavioral AI because it balances security, accuracy, and user experience, reducing false positives and unnecessary lockouts.
NEW QUESTION # 40
(Which platform is best for handling traffic surges and maintaining application availability across multi-cloud environments?)
- A. OpenStack
- B. Kubernetes
- C. Ansible
- D. AI Engine
Answer: B
Explanation:
Kubernetes is the most suitable platform for handling traffic surges and maintaining high application availability across multi-cloud environments. According to AI+ Network architecture principles, Kubernetes is designed as a cloud-native orchestration platform that automates container deployment, scaling, and management across distributed infrastructures. One of its core strengths ishorizontal auto-scaling, which dynamically increases or decreases application pods based on real-time metrics such as CPU utilization, memory usage, or custom telemetry. This makes Kubernetes highly effective during sudden traffic spikes.
In multi-cloud environments, Kubernetes provides a consistent control plane abstraction across different cloud providers, enabling workload portability and resilience. AI+ Network documentation emphasizes Kubernetes' support forself-healing, where failed containers are automatically restarted or rescheduled without manual intervention, ensuring continuous application availability. Additionally, Kubernetes integrates seamlessly with cloud-native load balancers and service meshes, allowing intelligent traffic distribution and failover across regions and providers.
Compared to OpenStack, which focuses on infrastructure provisioning, or Ansible, which is primarily a configuration automation tool, Kubernetes directly manages application runtime behavior at scale. AI Engines, while valuable for analytics, do not provide orchestration capabilities. Therefore, Kubernetes stands out as the optimal platform for maintaining performance, scalability, and availability in modern, AI-driven, multi-cloud network architectures.
NEW QUESTION # 41
(Which feature of Zero Trust Architecture best addresses insider threats by enforcing dynamic and continuous access controls?)
- A. Network perimeter segmentation
- B. Role-Based Access Control (RBAC)
- C. Firewalls to block unverified internal traffic
- D. Static IP-based rules
Answer: B
Explanation:
Role-Based Access Control (RBAC) is a key Zero Trust Architecture feature that effectively addresses insider threats through dynamic and continuous access enforcement. AI+ Network security documentation explains that RBAC limits user access based on defined roles and responsibilities, ensuring users can only access resources necessary for their job functions.
In a Zero Trust model, RBAC is continuously evaluated alongside contextual factors such as device posture, user behavior, and session risk. This reduces the potential damage from compromised insider accounts and prevents privilege abuse.
Static IP rules and perimeter segmentation rely on outdated trust assumptions, while firewalls alone cannot address insider misuse. AI+ Network materials identify RBAC as a foundational mechanism for enforcing least-privilege access within Zero Trust frameworks.
NEW QUESTION # 42
(How does AIEngine improve network traffic management?)
- A. Preempts security threats in web applications and APIs.
- B. Automates deep learning model deployment across devices.
- C. Enables programmable packet inspection and automation.
- D. Enhances network slicing for 5G traffic optimization.
Answer: C
Explanation:
AIEngine improves network traffic management by enabling programmable packet inspection and automation. According to AI+ Network documentation, AIEngine functions as an intelligent control layer that integrates analytics, policy enforcement, and automation into the data plane. By inspecting packets programmatically, AIEngine can identify traffic patterns, application types, and anomalies in real time.
This capability allows the network to automatically apply policies such as traffic prioritization, rate limiting, or rerouting without manual configuration. AIEngine leverages AI-driven insights to adapt network behavior dynamically based on live conditions, improving throughput, reducing congestion, and maintaining service quality.
While network slicing is specific to 5G architectures and security threat prevention focuses on application- layer protection, AIEngine's core value lies intraffic-aware automationat the network level. It does not deploy ML models directly, but instead uses AI outputs to control forwarding behavior. AI+ Network materials emphasize AIEngine as a key enabler of intent-based and self-optimizing networks.
NEW QUESTION # 43
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