Real Cisco 300-640 practice exam questions for easy pass!
Updated: Sep 02, 2026
No. of Questions: 82 Questions & Answers with Testing Engine
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| Certification Vendor: | Cisco |
|---|---|
| Exam Name: | Implementing Cisco Data Center AI Infrastructure |
| Exam Number: | 300-640 DCAI |
| Available Languages: | English |
| Passing Score: | Variable (approx. 750-850 out of 1000) |
| Certificate Validity Period: | 3 years |
| Exam Format: | Simulation, Multiple choice, Multiple select, Drag and drop |
| Exam Price: | USD 300 |
| Related Certifications: | CCNP Data Center Cisco Certified Specialist - Data Center AI Infrastructure |
| Real Exam Qty: | 55-65 |
| Exam Duration: | 90 minutes |
| Recommended Training: | Implementing Cisco Data Center AI Infrastructure (DCAI) Training Implementing and Operating Cisco Data Center Core Technologies (DCCOR) |
| Exam Registration: | Cisco Official Exam Registration |
| Sample Questions: | Cisco 300-640 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: knowledge of Cisco UCS, Nexus switching, and data center core technologies; 3–5 years of data center experience |
| Official Syllabus URL: | https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcai.html |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements |
| Topic 3: AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments |
| Topic 4: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability |
Question 1
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?
A. IGMP
B. CDP
C. ARP
D. LLDP
Question 2
A network team is configuring quality of service (QoS) for RoCEv2 traffic on a VXLAN fabric. The team must ensure that congestion notification packets (CNP) generated by storage targets are never dropped in the network, as these packets are critical for congestion control feedback. How must CNP packets be handled in the QoS configuration to ensure reliable delivery?
A. Enable Priority Flow Control specifically for CNP packets on a dedicated CoS value.
B. Configure CNP packets to use the same queue as regular RoCEv2 data traffic.
C. Classify CNP packets to a high-priority queue using their DSCP marking.
D. Apply traffic shaping to CNP packets to prevent them from causing additional congestion.
Question 3
A Cisco AI environment requires synchronized packet processing timestamps across network devices for analytics accuracy. Which protocol should be deployed?
A. NTP
B. PTP
C. DNS
D. FTP
Question 4
A network architect designs connectivity for a new Cisco AI POD operating in Intersight Managed Mode. To ensure high availability and aggregate bandwidth for external network traffic, a port channel must be established using multiple 100 GbE uplink ports on the fabric interconnects to connect to the upstream core switch infrastructure. Which specific policy, applied through the domain profile, must be modified to define and configure this port channel on the fabric interconnects for the desired uplink connectivity?
A. LAN connectivity policy
B. network connectivity policy
C. Ethernet network group
D. port policy
Question 5
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. 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
D. 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
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: C |
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