Cisco 300-745 Practice Test Questions Answers

The spread of malware across a sensitive segment like themanagement networkhighlights a failure in host-level security and internal visibility. To detect and mitigate the spread of such threats and protect the overall network,Endpoint Detection and Response (EDR)is the most effective choice among the options. In the Cisco security ecosystem, the endpoint is often the last line of defense and the most critical source of telemetry for malware incidents.

By deploying an EDR solution likeCisco Secure Endpoint, the manufacturing company gains the ability to identify the "patient zero" of the infection. EDR uses advanced features likeDevice TraversalandLateral Movementdetection to see how malware moves from one machine to another over the management network. Once detected, the security team can use the EDR platform to initiate a "host isolation" command, effectively cutting off the infected device's communication with the rest of the network without physically unplugging it. WhileEncrypted Threat Analytics (ETA)(Option C) is a powerful network-based feature for detecting malware in encrypted traffic without decryption, EDR provides the most granular control and response capabilities specifically for malwareresiding on and spreading betweenhosts. RADIUS (Option B) and IPsec VPNs (Option D) focus on access control and encryption of data in transit, respectively, but do not provide the behavioral analysis needed to stop a running malware outbreak once the network has already been accessed.

In the architectural design of a modern Security Operations Center (SOC), visibility is paramount.Splunkis a leading Security Information and Event Management (SIEM) and log management platform used to aggregate data from disparate sources across the enterprise. According to theCisco SDSI v1.0objectives, specifically within the "Risk, Events, and Requirements" domain, a central repository for telemetry is essential for incident response and threat hunting.

Splunk collects logs, metrics, and other data from network devices (firewalls, switches, routers), endpoints (laptops, servers), and cloud applications. It then indexes this data, allowing security analysts to perform complex searches, create visualizations, and build dashboards that provide a real-time view of the organization's security posture.

While Cisco offers native tools likeCisco Secure Cloud AnalyticsorCloud Observability(Option B) for specific cloud and application performance monitoring, Splunk serves as the broader "single pane of glass" for the entire infrastructure.Cisco Email Security Appliance(Option A) andCisco Web Security Appliance(Option C) are specialized security engines thatgeneratelogs but do not function as the overarching collection and analysis platform for the entire enterprise. By integrating Cisco security products with Splunk, organizations can correlate events—such as a blocked web request from a WSA and a malware alert from a Secure Endpoint—to identify a coordinated attack, fulfilling the Cisco SAFE requirement for pervasive visibility.

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In a Kubernetes environment,Namespacesprovide a logical partition for resources but do not, by default, provide network isolation. To prevent "app01" from communicating with "app02," aNetworkPolicymust be implemented. NetworkPolicies act as the Layer 3/4 distributed firewall for the cluster, allowing administrators to define explicit rules for ingress and egress traffic between pods and namespaces.

To achieve complete isolation, a common design pattern is to implement a "deny-all" default policy for each namespace and then explicitly allow only necessary traffic. This aligns with theCisco SAFEarchitectural principle of micro-segmentation. WhileRoleBinding(Option B) manages permissions for the Kubernetes API (who can create or delete pods), it does not control the actual network traffic between those pods.HTTPRoute(Option A) andGateway(Option D) are components of the Kubernetes Gateway API used for managing external traffic routing and load balancing, rather than internal pod-to-pod isolation. By deploying NetworkPolicies, the administrator ensures that the "blast radius" of a compromised application is contained within its own namespace, fulfilling a core objective of securing cloud-native application infrastructure.

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According to the Cisco SDSI v1.0 objectives, Artificial Intelligence and Machine Learning (ML) provide significant benefits in automating the identification of complex security weaknesses. One of the primary benefits is the ability of AI to performEncrypted Threat Analytics (ETA). AI models can analyze the metadata and initial handshake patterns of encrypted traffic—without needing to decrypt it—toidentify vulnerabilities associated with weak TLS algorithmsor outdated cipher suites.

By recognizing specific fingerprints in the TLS handshake, AI-driven tools can alert administrators to non-compliant encryption standards that might be susceptible to interception. While AI is a powerful force multiplier, it doesnot replacea comprehensive defense-in-depth strategy (Option B); rather, it enhances it. It does not directlyspeed up data transmission(Option A), as that is a function of hardware and bandwidth. Furthermore, while AI helps mitigate DDoS attacks, it rarely provides "complete" protection (Option C) on its own, as DDoS mitigation requires a multi-layered approach involving massive bandwidth and specialized scrubbing. The ability to identify cryptographic weaknesses at scale is a core functional benefit of AI in modern security infrastructure, aligning with the Cisco goal of maintaining a hardened and compliant network posture through automated visibility.

In the architecture of modern security, Artificial Intelligence (AI) and Machine Learning (ML) are leveraged to move beyond reactive, signature-based defenses. One of the most significant uses of AI in securing network infrastructure is the detection ofzero-day attacks(often referred to in exam contexts as "day zero" attacks). A zero-day attack exploits a vulnerability that is unknown to the software vendor or the public, meaning no signature exists for traditional firewalls or antivirus software to block it.

AI identifies these threats throughbehavioral analysisandanomaly detection. By establishing a highly granular baseline of "normal" network traffic patterns—including flow direction, packet size, inter-packet arrival times, and protocol behavior—AI models can detect subtle deviations that indicate a malicious exploit. For example,Cisco Secure Network Analytics(formerly Stealthwatch) andEncrypted Threat Analytics (ETA)use ML to identify the cryptographic "fingerprints" of malware even within encrypted traffic, without the need for decryption. This allows the security infrastructure to identify and mitigate threats at the moment they appear, rather than waiting for a vendor to release a signature. While load balancing (Option B), traffic shaping (Option C), and Quality of Service (Option D) are critical for network performance and availability, they are traditional traffic engineering functions that do not inherently provide the advanced threat detection capabilities offered by AI-driven security models. Within the Cisco SDSI objectives, AI is positioned as the primary technology for achieving proactive visibility and reducing the "Mean Time to Detect" (MTTD) for previously unseen vulnerabilities.