CCNA AI and Network Operations Practice Test 7 — 15 Questions

Question 1

You are connected to R1, a Cisco IOS-XE router that provides connectivity for the 192.168.10.0/24 network. The network operations team uses a Python script with netmiko to push configuration changes. However, the script fails to connect to R1. You need to verify the SSH configuration on R1.

Accepted Answer

The VTY lines are configured for telnet only; SSH requires 'transport input ssh'.. The most likely cause is that the VTY lines are configured to accept only Telnet, not SSH. By default, Cisco IOS VTY lines accept both Telnet and SSH if SSH is enabled, but if 'transport input telnet' is configured, SSH connections are refused. The script using netmiko attempts SSH, so it fails. Verifying the VTY configuration is the first step.

Answer

The SSH server is not enabled because the crypto key is missing or 'ip ssh' is not configured.

Answer

The SSH version is set to version 1, which is not supported by the Python script.

Answer

The domain name is not configured, which is required for SSH key generation.

Question 2

You are connected to R1 via the console. R1 is a Cisco ISR 4321 router running IOS-XE 17.3. The network team has reported intermittent connectivity issues between VLAN 10 hosts and the server at 10.0.0.100. You suspect a routing problem and need to analyze the IP routing table, ARP cache, and interface status to identify the cause. Use the provided outputs to diagnose the issue.

Accepted Answer

The routing table shows a default route via 192.168.1.1, but the ARP cache has an incomplete entry for that next-hop IP, indicating a Layer 2 connectivity issue.. An incomplete ARP entry for the next-hop IP indicates that the router cannot resolve the MAC address for that IP, which points to a Layer 2 connectivity issue (e.g., wrong VLAN, cabling, or switch port problem). Since the routing table has a default route via that next-hop, the routing decision is correct, but the packet cannot be forwarded due to ARP failure.

Answer

The routing table has a static route to 10.0.0.0/24 via 192.168.1.2, but the interface GigabitEthernet0/0/0 is administratively down.

Answer

The routing table shows a route to 10.0.0.0/16 via 192.168.1.1, but the ARP cache has a complete entry for 192.168.1.1, indicating the issue is at Layer 3.

Answer

The routing table has no route to 10.0.0.100, and the ARP cache is empty for all entries, indicating a complete routing failure.

Question 3

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

Answer

Eliminating the need for network monitoring tools entirely

Answer

Automating the physical installation of network cables

Question 4

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

Answer

AI completely replacing the need for network engineers

Answer

AI automatically resolving all network congestion issues without human input

Question 5

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.

Answer

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

Answer

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

Answer

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

Question 6

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 7

Which four of the following correctly describe how AI/ML techniques can improve network operations in a modern enterprise? (Choose all that apply.)

Accepted Answer

AI models can analyze historical traffic data to predict future bandwidth congestion. Options A, B, C, and D are correct. AI models can predict bandwidth congestion by analyzing historical traffic data, enabling proactive capacity planning. Natural language processing (NLP) automates helpdesk ticket responses by interpreting user intent, reducing manual effort. Reinforcement learning can dynamically adjust firewall rules in response to evolving attack patterns, improving threat response without human intervention. Unsupervised learning can cluster behavior patterns to identify unknown device types on the network. Option E ("AI eliminates the need for baseline performance metrics because it learns in real-time") is incorrect because even AI/ML models require baseline metrics to establish normal behavior and detect anomalies; real-time learning does not remove the need for baselines. Option F ("ML models always require labeled training data to be effective in network operations") is incorrect because many ML techniques, such as unsupervised learning (as shown in option D), operate effectively on unlabeled data by discovering patterns and clusters without predefined labels.

Answer

AI eliminates the need for baseline performance metrics because it learns in real-time

Answer

ML models always require labeled training data to be effective in network operations

Question 8

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

Answer

Replacing SNMP with AI-generated proprietary agents on every device

Answer

Eliminating the need for network logs by using only synthetic data

Question 9

Which three of the following are typical use cases for automation in network security operations? (Choose three.)

Accepted Answer

Correlating authentication logs with network flows to detect lateral movement by an attacker. The three correct options represent realistic automation use cases in network security operations. Automated identification and blocking of malware variants using behavioral analysis leverages machine learning to detect zero-day threats without relying on signatures. Correlating authentication logs with network flows allows security teams to identify lateral movement by spotting anomalous access patterns, which is a key technique in threat hunting. Dynamic micro-segmentation adjusts policies in real-time based on endpoint risk scores, enabling instant isolation of compromised devices. The incorrect options are unrealistic: a single AI-generated rule cannot cover all traffic, AI cannot guarantee zero false positives, and disabling all ports on any anomaly would cause denial of service.

Answer

Guaranteeing zero false positives in intrusion detection by using deep learning

Answer

Automatically disabling all network ports when any anomaly is detected

Answer

Replacing all firewall rules with a single AI-generated rule that covers all traffic

Question 10

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

Accepted Answer

Predicting traffic congestion and proactively rerouting flows to avoid bottlenecks. 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

Answer

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

Answer

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

Question 11

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

Answer

Static threshold-based alerting for interface errors

Question 12

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

Answer

Guaranteed elimination of all false positive alarms in the network

Question 13

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 14

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.

Answer

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

Question 15

A network administrator is configuring a Cisco IOS XE router to support model-driven telemetry using gRPC dial-out. The administrator wants to stream interface statistics every 30 seconds to a collector at 192.168.1.100 on port 57000 using JSON encoding. Which configuration snippet correctly sets up the telemetry subscription?

Accepted Answer

telemetry ietf subscription 1
 encoding encode-json
 stream yang-push
 filter xpath /interfaces/interface/statistics
 update-policy periodic 3000
 receiver ip address 192.168.1.100 57000. The correct telemetry subscription requires specifying the encoding, stream, filter, update policy, and receiver. The update-policy periodic value is in centiseconds, so 3000 corresponds to 30 seconds. The source-address is optional and not needed if the router can determine its outgoing interface. Using on-change would not meet the periodic requirement.

Answer

telemetry ietf subscription 1
 encoding encode-json
 source-address 192.168.1.100
 stream yang-push
 filter xpath /interfaces/interface/statistics
 update-policy periodic 3000
 receiver ip address 192.168.1.100 57000

Answer

telemetry ietf subscription 1
 encoding encode-json
 stream yang-push
 filter xpath /interfaces/interface/statistics
 update-policy periodic 30
 receiver ip address 192.168.1.100 57000

Answer

telemetry ietf subscription 1
 encoding encode-json
 stream yang-push
 filter xpath /interfaces/interface/statistics
 update-policy on-change
 receiver ip address 192.168.1.100 57000