Cisco 300-640 Questions & Answers - in .pdf
- Total Q&A: 82
- Update: Sep 09, 2026
- Price: $59.99
- Vendor: Cisco
- Exam Code: 300-640
- Exam Name: Implementing Cisco Data Center AI Infrastructure
- Features:
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- Complete Cisco Recommended Syllabus.
- Free 300-640 PDF Demo Available.
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- Exact Cisco 300-640 Exam Questions with Correct Answers, verified by Experts with years of Experience in IT Field.
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Cisco 300-640 Q&A - Testing Engine
- Total Q&A: 82
- Update: Sep 09, 2026
- Price: $59.99
- Vendor: Cisco
- Exam Code: 300-640
- Exam Name: Implementing Cisco Data Center AI Infrastructure
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.Cisco 300-640 Exam Overview:
| Certification Vendor: | Cisco |
|---|---|
| Exam Name: | Implementing Cisco Data Center AI Infrastructure |
| Exam Number: | 300-640 DCAI |
| Real Exam Qty: | 55-65 |
| Related Certifications: | CCNP Data Center Cisco Certified Specialist - Data Center AI Infrastructure |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 300 |
| Passing Score: | Variable (approx. 750-850 out of 1000) |
| Available Languages: | English |
| Exam Format: | Simulation, Multiple choice, Drag and drop, Multiple select |
| 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 |
Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation - Manage health, logs, and events |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools |
| Topic 4: AI Infrastructure Fundamentals | 20% | - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments - Describe AI and machine learning concepts - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
Question #1
An end customer plans to deploy an AI fabric in a new data center. Their engineers are more familiar with CLI-based networks and require the capability to automate standardized configuration templates and monitor flows for the fabric. Which data center automation tool meets the requirements?
A. Intersight
B. Nexus Dashboard
C. Digital Network Architecture Center
D. HyperFabric
Question #2
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?
A. PFC pause frame transmission rate per interface
B. VXLAN tunnel packet encapsulation rate
C. BGP EVPN route count per leaf switch
D. spanning tree topology change notifications
Question #3
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
A. Elimination of routing protocols
B. Simplified wireless integration
C. Predictable low-latency east-west traffic
D. Reduced need for IP addressing
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. network connectivity policy
B. port policy
C. LAN connectivity policy
D. Ethernet network group
Question #5
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?
A. LLDP
B. ARP
C. CDP
D. IGMP
Solutions:
| Question #1 Answer: B | Question #2 Answer: A | Question #3 Answer: C | Question #4 Answer: B | Question #5 Answer: D |
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