CCNA AI and Network Operations Practice Test 7 — 15 Questions

Question 1

Which three of the following are key applications of AI in network operations? (Choose three.)

Accepted Answer

Anomaly detection and proactive threat identification. AI in network operations enhances efficiency by automating complex analytical tasks. Anomaly detection uses machine learning models to identify deviations from baseline traffic patterns, enabling proactive threat identification before they cause outages. Automated root cause analysis correlates events across the network to pinpoint the origin of a fault, reducing mean time to repair (MTTR). Predictive maintenance leverages telemetry data (e.g., from SNMP, NetFlow, or gRPC) to forecast hardware failures, allowing preemptive replacement and minimizing downtime. In contrast, AI does not replace all manual CLI configuration with AI-generated scripts (option D) because many nuanced configurations require human oversight; it does not eliminate the need for network monitoring tools (option E) because AI depends on these tools for data; and it cannot automate physical installation of cables (option F), as that is a manual, physical task.

Answer

Replacing all manual CLI configuration with AI-generated scripts

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Eliminating the need for network monitoring tools entirely

Answer

Automating the physical installation of network cables

Question 2

Which three of the following are common challenges when deploying AI in network operations? (Choose three.)

Accepted Answer

Ensuring high-quality, clean training data for machine learning models. Ensuring high-quality, clean training data is a fundamental challenge because AI/ML models depend on accurate, labeled data to learn patterns; poor data leads to unreliable predictions. Integrating AI tools with legacy systems is difficult due to proprietary APIs and outdated protocols, often requiring custom middleware. Avoiding false positives is critical because excessive alarms desensitize operators and waste resources. The three incorrect options are not real deployment challenges: 'AI models always requiring cloud connectivity' is false because on-premise and edge AI can function offline; 'AI completely replacing network engineers' is a misconception—AI augments, not replaces, engineers; and 'AI automatically resolving all congestion without human input' is an unrealistic expectation, not a common challenge in practice.

Answer

AI models always requiring cloud connectivity to function

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AI completely replacing the need for network engineers

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AI automatically resolving all network congestion issues without human input

Question 3

Which three options describe how AI contributes to network automation and orchestration? (Choose three.)

Accepted Answer

AI can optimize routing paths dynamically based on real-time traffic analysis. AI contributes to network automation and orchestration by enabling dynamic optimization of routing paths based on real-time traffic analysis, automatically adjusting QoS policies to prioritize critical application traffic, and predicting link failures to trigger preemptive rerouting. These capabilities leverage machine learning models to analyze network telemetry data, such as NetFlow or SNMP, and make automated decisions that improve performance, reliability, and efficiency without manual intervention.

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AI eliminates the need for any intent-based networking (IBN) frameworks

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AI can replace all configuration templates with entirely self-generated configs

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AI ensures that every network change is 100% error-free without testing

Question 4

Which four of the following are common use cases or features of AI and Machine Learning in network operations? (Choose all that apply.)

Accepted Answer

Anomaly detection for security threats. Anomaly detection for security threats uses ML to identify deviations from normal traffic patterns, enabling proactive threat identification. Automated root cause analysis leverages AI to correlate events and faults across the network to quickly pinpoint the source of issues. Predictive maintenance applies ML models to historical hardware data (e.g., error logs, temperature) to forecast failures before they occur. Dynamic traffic routing uses reinforcement learning or other AI techniques to adapt routing decisions in real time based on changing network conditions. The two incorrect options are not AI/ML use cases: manual VLAN configuration is a static, rule-based task that does not involve learning or adaptation, and replacing all network engineers with fully autonomous AI is an unrealistic, futuristic concept not representative of current AI/ML applications in network operations.

Answer

Manual configuration of static VLANs on access switches

Answer

Replacing all network engineers with fully autonomous AI

Question 5

Which three options describe common applications of AI/ML in network telemetry and monitoring? (Choose three.)

Accepted Answer

Baseline profiling to detect unusual traffic patterns that may indicate an attack. Baseline profiling (correct) uses machine learning to learn normal traffic patterns and detect anomalies like attacks. Dynamic threshold tuning (correct) leverages learned behavior to adjust thresholds automatically, reducing false positives. Automated root cause analysis (correct) correlates events across devices using AI to identify the source of issues. Directly rewriting routing tables in OSPF (wrong) is not an AI/ML application—OSPF has its own protocol mechanisms, and AI would not bypass them without integration. Replacing SNMP with AI-generated proprietary agents (wrong) is impractical and unnecessary; AI enhances rather than replaces standard protocols. Eliminating network logs with synthetic data (wrong) contradicts monitoring needs; logs remain essential for audit and analysis, and AI uses real data for training.

Answer

Directly rewriting routing tables in OSPF without any protocol interaction

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Replacing SNMP with AI-generated proprietary agents on every device

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Eliminating the need for network logs by using only synthetic data

Question 6

Which three options correctly describe how AI can optimize network performance and quality of service (QoS)? (Choose three.)

Accepted Answer

Automatically adjusting QoS queue weights based on learned application traffic patterns. AI optimizes network performance and QoS by analyzing traffic patterns and making real-time adjustments. Predicting congestion and rerouting flows prevents packet loss and delays. Automatically adjusting queue weights ensures bandwidth is allocated based on learned application needs. Identifying and prioritizing latency-sensitive traffic like VoIP ensures low jitter and delay, meeting QoS requirements. The incorrect options are unrealistic: replacing all hardware switches with software-based AI routers cannot eliminate latency because hardware still provides fast forwarding; AI cannot guarantee line-rate throughput on all interfaces regardless of traffic load because bandwidth is limited; and AI cannot entirely eliminate packet loss by predicting every transmission failure because physical and unpredictable errors still occur.

Answer

Replacing all hardware switches with software-based AI routers to eliminate latency

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Guaranteeing line-rate throughput on all interfaces regardless of traffic load

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Eliminating packet loss entirely by using AI to predict every transmission failure

Question 7

Which three of the following are key applications of AI and machine learning in modern network operations? (Choose three.)

Accepted Answer

Automated anomaly detection and root cause analysis. Automated anomaly detection and root cause analysis are key AI/ML applications because they continuously analyze telemetry data to spot deviations and pinpoint failures without manual intervention, a core AIOps capability. Predictive capacity planning uses ML on traffic trends to forecast bandwidth needs and avoid congestion before it occurs. Real-time security threat identification via behavioral analysis applies machine learning to baseline normal traffic and flag unusual patterns indicative of attacks. In contrast, dynamic routing protocol configuration using reinforcement learning is not yet a standard key application in modern network operations; it is still experimental and not widely deployed. Fully autonomous AI replacing all manual configuration is not operationally realistic. Static threshold-based alerting is a simple rule-based method that does not involve ML.

Answer

Replacing all manual network configuration with fully autonomous AI

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Static threshold-based alerting for interface errors

Question 8

A network engineer is implementing AIOps to improve network reliability. Which four of the following are core capabilities that AIOps platforms typically provide? (Choose four.)

Accepted Answer

Correlation of alerts from multiple network devices to reduce noise. Correlation of alerts from multiple network devices is a core AIOps capability because it uses machine learning to analyze and group related alerts, reducing noise and helping engineers focus on root causes rather than individual symptoms. Self-healing by automatically reverting problematic configuration changes is a key feature, as AIOps can detect anomalies caused by changes and roll back to a stable state to minimize downtime. Predictive maintenance identifies devices likely to fail by analyzing historical performance data and telemetry, enabling proactive replacement or repair before failures occur. Baseline learning of normal traffic patterns allows AIOps to detect deviations that indicate potential issues. The incorrect options are not core AIOps capabilities: Direct manipulation of physical layer hardware is beyond the scope of AIOps, which focuses on software-based analysis and automation; guaranteed elimination of all false positives is unrealistic because no system can achieve 100% accuracy in alarm filtering.

Answer

Direct manipulation of physical layer hardware to replace faulty optics

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Guaranteed elimination of all false positive alarms in the network

Question 9

Which TWO of the following statements correctly describe REST API operations?

Accepted Answer

API keys and OAuth tokens are common methods for authenticating REST API requests.. REST APIs commonly use API keys and OAuth tokens for authentication. API keys are simple, static tokens passed in headers or query strings, while OAuth 2.0 provides token-based authorization with scoped access. Additionally, REST APIs rely on standard HTTP status codes (e.g., 200 OK, 404 Not Found) to indicate the outcome of a request, making them a universal signaling mechanism.

Answer

The HTTP PUT method is used to retrieve a resource representation.

Answer

JSON is the only data format supported by REST APIs.

Answer

The HTTP DELETE method corresponds to the Update operation in CRUD.

Question 10

Drag and drop the following steps into the correct order to retrieve network device information using a REST API call that requires authentication and returns JSON data.

Accepted Answer

Determine the REST API endpoint and the appropriate HTTP method (e.g., GET for reading data).. The correct sequence follows the standard REST API client workflow: first, identify the API endpoint and HTTP method; then prepare the request by including authentication credentials and setting the Accept header (these two steps can be in any order); send the request; verify the HTTP status code; and finally parse the response body.

Question 11

Select all statements that correctly describe the automation tools and concepts.

Accepted Answer

Puppet: A pull-based configuration tool using a proprietary DSL.. Puppet is a tool employing a pull mechanism for node configuration using a proprietary DSL. Chef is a framework that uses Ruby-coded recipes within a client-server deployment model. Ansible is a solution providing agentless orchestration via SSH and YAML-written playbooks. Python is a high-level programming language utilized for scripting network automation tasks. REST API is a web-based interface for programmatic device management using HTTP methods.

Answer

Chef: An agentless automation tool that uses SSH and YAML.

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Ansible: A configuration management tool that uses Ruby recipes and a client-server model.

Question 12

An API client sends a valid GET request and receives an HTTP 200 response. What does that indicate?

Accepted Answer

The request was successful. HTTP 200 indicates a successful request. For a GET operation, it generally means the server successfully returned the requested resource representation.

Answer

The resource was deleted successfully

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Authentication permanently failed

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The server requires a reboot

Question 13

Why is version control valuable for network automation files?

Accepted Answer

It tracks changes and allows rollback to earlier versions. Version control systems help teams track who changed what, compare revisions, and restore earlier states when needed. That improves operational discipline but does not guarantee correctness by itself.

Answer

It increases interface bandwidth

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It replaces the need for device backups

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It guarantees that configuration changes are error free

Question 14

Why is version control useful for network automation scripts?

Accepted Answer

It tracks changes and supports rollback and review. Version control provides change history, collaboration, and rollback options for scripts and infrastructure-as-code files.

Answer

It automatically fixes coding mistakes

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It removes the need for API authentication

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It replaces the device operating system

Question 15

What is a main operational benefit of a controller-based networking architecture?

Accepted Answer

It centralizes policy and can simplify network-wide changes. Controller-based architectures centralize the control plane, enabling network-wide policy management and simplifying changes. This is the correct answer because it accurately describes the primary operational benefit. Option A is wrong because controller-based architectures still require IP addressing for management and communication. Option C is wrong because the data plane on switches remains operational for local forwarding; only the control plane may be centralized. Option D is wrong because dynamic routing protocols can still be used, and routes are not forced to be static.

Answer

It removes the need for IP addressing

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It eliminates the data plane on switches

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It forces all routes to become static