CCNA AI and Network Operations Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  AI can analyze network state and suggest or enforce configuration changes to meet defined business intent

  Why this is correct

  In intent-based networking, AI continuously ingests telemetry from devices, flows, and policies to build a real-time model of network state. When the observed state deviates from desired business outcomes, AI-driven closed-loop systems can recommend or directly apply configuration changes via NETCONF/RESTCONF APIs, reducing human reaction time and enabling dynamic optimization.

• ✓

  IBN systems may use AI to verify that the current network state matches the desired intent

  Why this is correct

  IBN's assurance layer relies on AI/ML to compare actual forwarding behavior, device configurations, and policy compliance against the intent model. This verification is continuous, catching issues such as packet drops, policy misconfigurations, or path deviations that static checks miss, and it feeds back into the automation loop for remediation.

• ✓

  AI can assist in translating high-level business policies into low-level device configurations

  Why this is correct

  High-level intent, like guaranteeing latency for video conferencing, requires mapping to specific QoS classes, queues, ACLs, and routing policies across heterogeneous devices. AI helps infer the necessary device-specific CLI or YANG models from the abstract policy, and validates the translation to ensure consistency and correct vendor syntax.

• ✗

  SDN controllers replace AI for all decision-making, making AI unnecessary in IBN

  Why it's wrong here

  SDN controllers centralize control by providing APIs and a network-wide view for programmatic policy, but they are deterministic engines that execute defined logic, not autonomous decision-makers. AI adds the ability to learn from historical data, predict anomalies, and optimize intent assurance, so IBN leverages both rather than replacing one with the other.

• ✗

  AI in SDN is only used for traffic engineering, not for security or compliance

  Why it's wrong here

  While AI has popular traffic engineering applications like dynamic path optimization and load balancing, its role extends to security monitoring, anomaly detection, and compliance auditing by analyzing flow logs and configuration drift. AI-driven systems identify threats, validate policy adherence, and suggest corrections, so limiting AI's scope to traffic engineering mischaracterizes its broad utility in IBN.

• ✗

  Intent-based networking eliminates the need for any monitoring or feedback loops

  Why it's wrong here

  Intent is not a set-and-forget mechanism; it requires continuous monitoring to detect when the network deviates from the declared business intent. IBN relies on an active feedback loop where telemetry is gathered, analyzed, and used for closed-loop remediation; removing monitoring would blind the system to performance degradation, policy violations, or failures, defeating the very purpose of IBN.

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