300-640 Exam Guide
This 300-640 exam focuses on practical knowledge and real-world application scenarios related to the subject area. It evaluates your ability to understand core concepts, apply best practices, and make informed decisions in realistic situations rather than relying solely on memorization.
This page provides a structured exam guide, including exam focus areas, skills measured, preparation recommendations, and practice questions with explanations to support effective learning.
Exam Overview
The 300-640 exam typically emphasizes how concepts are used in professional environments, testing both theoretical understanding and practical problem-solving skills.
Skills Measured
- Understanding of core concepts and terminology
- Ability to apply knowledge to practical scenarios
- Analysis and evaluation of solution options
- Identification of best practices and common use cases
Preparation Tips
Successful candidates combine conceptual understanding with hands-on practice. Reviewing measured skills and working through scenario-based questions is strongly recommended.
Practice Questions for 300-640 Exam
The following practice questions are designed to reinforce key 300-640 exam concepts and reflect common scenario-based decision points tested in the certification.
Question#1
An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements: Support 256 GPU servers with RDMA connectivity.
Prevent any packet loss that causes RDMA connection failures.
Maintain consistent low-latency communication with a target of less than 10 microseconds.
Use industry-standard protocols and configurations.
Which configuration ensures that RoCEv2 operates as a lossless transport?
A. Implement traffic shaping to smooth RDMA traffic bursts and prevent congestion.
B. Enable Priority Flow Control on the traffic class carrying RoCEv2 packets.
C. Set the RDMA traffic to use the highest DSCP value to ensure priority forwarding.
D. Configure weighted fair queuing to prioritize RDMA traffic over other traffic classes.
Question#2
A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment.
Which Cisco Intersight capability is most critical for effectively monitoring this AI-centric infrastructure?
A. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
B. integration with Cisco Nexus Dashboard for advanced network flow analytics and anomaly detection across the data center fabric, which ensures high-speed data transfer between GPU nodes
C. basic server health reporting, which is limited to CPU and memory statistics, and requires specialized third-party tools for any GPU-specific performance monitoring or diagnostics
D. comprehensive health and performance monitoring, which provides real-time telemetry data for UCS servers, including granular metrics for NVIDIA GPUs, which enables direct insights into AI workload execution
Question#3
A company is building a network fabric for an AI training cluster that will train large language models. The cluster includes 256 GPUs distributed across 32 servers. Security requirements mandate isolation between different training projects and allowing shared access to a central storage system.
Which approach provides the required security isolation and maintains optimal performance for GPU-to-GPU communication?
A. Deploy separate physical networks for each project with dedicated switches and cross-connects to shared storage.
B. Use VXLAN with separate VNIs per project and Group Policy Option to control granular access to shared storage.
C. Implement port-based VLANs for each project with router ACLs controlling storage access at the distribution layer.
D. Configure QoS-based traffic steering with separate DSCP markings per project and policy-based routing to shared storage.
Question#4
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?
A. distinct management for isolated components
B. limited connectivity within isolated locations
C. delayed rollout of new network resources
D. efficient launch of computational platforms
Question#5
What does workload distribution offer in an AI infrastructure with local and external resources?
A. isolation of analytic functions within remote processing clusters
B. distribution of computational tasks across multiple locations
C. restriction of automation to local storage and performance measures
D. enforcement of fixed security policies on distribution endpoints
Disclaimer
This page is for educational and exam preparation reference only. It is not affiliated with Cisco, CCNP Data Center, or the official exam provider. Candidates should refer to official documentation and training for authoritative information.
