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Cisco Certified Specialist Data Center AI Infrastructure Quick Facts
Achieving deep expertise in AI-ready data center environments opens exciting new possibilities for innovation and career advancement. This exam overview provides a clear roadmap to help you confidently prepare for the Cisco Certified Specialist Data Center AI Infrastructure certification and master the technologies powering next-generation AI workloads.
Exploring the Cisco Certified Specialist Data Center AI Infrastructure Certification
The Cisco Certified Specialist Data Center AI Infrastructure certification validates your ability to design, deploy, and operate the robust, high-performance environments required to support modern AI workloads. It focuses on integrating compute, storage, networking, and orchestration technologies across Cisco’s AI-ready ecosystem, including Nexus, UCS, Intersight, and Hyperfabric AI. This credential is ideal for data center professionals, AI infrastructure engineers, and systems architects seeking to lead adoption of AI-driven operations at scale.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: AI Fundamentals and Applications (20% of the exam)
Describe AI/ML workload types
- RAG
- Training
- Inference
- Generative AI
Summary: This section introduces the core workload types that shape AI ecosystems today, emphasizing how retrieval-augmented generation, model training, inference, and generative processes differ in resource requirements and design priorities. You’ll learn to link these workloads with the appropriate infrastructure characteristics for optimal performance and scalability.
In the exam, expect to demonstrate understanding of how AI pipelines evolve from data preparation to model deployment and how compute, storage, and networking considerations vary depending on whether you are training or serving models in real time.
Describe the types of AI infrastructure
- Cloud
- Hybrid
- On-premises
- Edge AI
Summary: This section explores where and how AI infrastructure can be deployed—whether in cloud, hybrid, on-premises, or edge environments. You’ll identify scenarios for each model and the factors that guide selection, such as latency, compliance, data gravity, and cost.
You’ll also gain insight into combining on-premises GPU clusters with public cloud resources to achieve the best balance of performance and flexibility, preparing you to recommend architectures suited to an organization’s AI maturity and business objectives.
Describe the components used for AI environments
- Network
- Compute and GPUs deployment (NVLink)
- Virtualization and containerization
- Orchestration
- Monitoring
- Storage such as SAN, Fibre Channel, NVMe, Block and File
Summary: This section defines the essential infrastructure building blocks that make AI workloads possible. You’ll explore data center networking designs optimized for large data transfers, GPU interconnect technologies like NVLink, and storage types that support parallel access and high throughput.
Core topics include virtualization, containerization, and the orchestration layers that maintain efficiency across clusters. You’ll see how these components combine into resilient environments that can handle the scale and velocity required by complex AI workloads.
Describe Cisco AI solutions
- AI PODs
- AI Canvas
- Hyperfabric AI
Summary: This section introduces Cisco’s portfolio of AI-focused solutions engineered to accelerate end-to-end AI deployment and lifecycle management. You’ll discover how AI PODs simplify infrastructure scaling, how AI Canvas streamlines workflow orchestration, and how Hyperfabric AI integrates network, compute, and storage.
The focus is on understanding the role each solution plays in a cohesive architecture, equipping you with the ability to design reliable, high-performance, and policy-driven AI environments using Cisco’s ecosystem of advanced data center technologies.
Describe AI lifecycle and use cases
- Describe the AI lifecycle
- Describe AI use cases
Summary: This section connects technical infrastructure capabilities with the broader AI lifecycle—from data ingestion and model development to deployment and continuous optimization. You’ll map AI workflows to the operational processes and tools needed at each phase.
You’ll also analyze real-world use cases where AI infrastructure provides tangible outcomes, such as predictive analytics, conversational AI, and computer vision. The emphasis is on understanding how Cisco infrastructure underpins business transformation through intelligent automation.
Domain 2: AI Infrastructure Components and Architecture (30% of the exam)
AI Infrastructure Components and Architecture
- Evaluate network deployment based on AI workload requirements such as bandwidth, latency, redundancy, scalability, and security
- Evaluate compute deployment based on AI workload requirements such as CPU resources, GPU resources and connectivity, memory, virtualization support, scalability, redundancy, and workload types
- Evaluate storage deployment based on AI workload requirements such as capacity, performance, redundancy and availability, and scalability
- Evaluate power, efficiency, and sustainability based on AI workload requirements such as power and cooling, power usage effectiveness, and renewable energy
- Evaluate hybrid AI deployment with cloud integration such as secure connectivity, data synchronization, and workload mobility
Summary: This section focuses on designing balanced and high-performing infrastructures capable of supporting variable AI workloads. You’ll evaluate how to align network bandwidth, compute power, and storage throughput to meet the precision and speed that AI demands. Key factors such as fault tolerance, redundancy, and application scalability are analyzed to ensure continuous operations even under intensive tasks.
You’ll also explore sustainable and hybrid architectures, understanding how energy efficiency, cooling design, and cloud integration play a role in robust deployments. This knowledge ensures you can architect solutions that are both technically sound and operationally efficient.
Domain 3: AI Infrastructure Deployment and Data Management (30% of the exam)
Configure high-performance networks to support AI workloads using Cisco Data Center
- Congestion control mechanisms (PFC, ECN, ETS)
- RDMA over Converged Ethernet (RoCE, RoCEv2)
- Quality of service (QoS)
- Load distribution
Summary: This section explores the network features that create the backbone of AI data center operations. You’ll study advanced transport enhancements such as priority flow control and explicit congestion notification to sustain low-latency data movement across fabrics optimized for GPU-driven tasks.
In addition, you’ll learn how to implement QoS and load distribution policies to maintain predictable and balanced network behavior. The emphasis is on achieving the throughput, reliability, and responsiveness essential for data-heavy AI training and inference.
Configure high-performance compute and storage to support AI workloads using Cisco UCS
- Domain profiles
- Power policy
- Storage policies
- LAN connectivity and vNIC policies
- QoS policies and system classes
- NTP policy
Summary: This section guides you through deploying AI-ready compute and storage solutions using Cisco UCS. You’ll configure domain profiles and power policies that deliver optimal GPU and CPU performance, while ensuring consistent redundancy and synchronization.
The focus extends to defining vNIC, LAN connectivity, and QoS system classes that uphold stable performance during large-scale AI processing. This knowledge prepares you to assemble unified, policy-driven compute domains that handle the rigorous demands of modern AI workloads.
Deploy AI-ready fabrics using Cisco orchestration tools
- Nexus Dashboard
- APIC
- Hyperfabric
- Intersight
Summary: This section centers on integrating automation and orchestration tools to deploy AI fabric environments that are secure, scalable, and easy to manage. You’ll configure Cisco orchestration platforms such as APIC and Nexus Dashboard to unify policy and performance control.
You’ll also explore how Hyperfabric and Intersight combine visibility and automation for streamlined operations. Mastery of these tools enables efficient lifecycle management and rapid scaling across complex infrastructures running AI workloads.
Domain 4: AI Infrastructure Operations and Troubleshooting (20% of the exam)
AI Infrastructure Operations and Troubleshooting
- Implement benchmarks to evaluate AI infrastructure performance
- Implement monitoring of AI data center infrastructures using Cisco solutions such as Nexus Dashboard and Intersight
- Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance — Operational telemetry
- Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance — System health
- Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance — Alerts
- Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance — Log correlation
- Troubleshoot AI infrastructure using system messages and management tools
Summary: This section emphasizes maintaining the health and reliability of AI infrastructure through strategic monitoring and diagnostics. You’ll implement benchmarks to gauge performance and use Cisco’s visibility tools to identify optimization opportunities across network, compute, and storage components.
The focus continues with telemetry, alerting, and log correlation, giving you the confidence to pinpoint and resolve operational issues efficiently. You’ll gain the skills needed to sustain consistent high availability and performance while ensuring that each AI workload operates at its full potential.
Who Should Pursue the Cisco Certified Specialist – Data Center AI Infrastructure Certification?
The Cisco Certified Specialist – Data Center AI Infrastructure (300-640 DCAI) certification is perfect for IT professionals looking to enhance their knowledge of AI-ready data center design, deployment, and management. It’s designed for:
- Data center administrators and network engineers advancing into AI infrastructure
- Systems engineers and solution architects focusing on AI workloads
- Cloud and hybrid data center professionals integrating AI capabilities
- IT consultants and operations managers overseeing high-performance infrastructure projects
Whether you’re expanding your expertise in Cisco technologies or entering the AI infrastructure space, this certification validates the advanced skill set that today’s intelligent data centers demand.
What Career Opportunities Can This Certification Help Unlock?
Earning your Cisco Data Center AI Infrastructure Specialist certification demonstrates your ability to build, manage, and troubleshoot AI-intensive ecosystems—an area rapidly growing in demand. Roles this certification supports include:
- AI Infrastructure Engineer
- Data Center Engineer or Administrator
- Cloud Integration Specialist
- AI Systems Architect
- Network Automation Engineer
This certification also strengthens your career path toward the CCNP Data Center certification, enabling broader specialization across Cisco technologies.
What Exam Code and Version Should I Know About?
The official exam code is 300-640 DCAI, currently at version 1.0. This version reflects Cisco’s modernized approach to supporting AI workloads with efficiency, scalability, and intelligence in the data center.
How Long Do You Have to Complete the Exam?
The DCAI 300-640 exam allows you 90 minutes to complete all questions. The timeframe gives you ample opportunity to analyze each scenario carefully and apply your understanding of Cisco’s AI infrastructure technologies effectively.
How Much Does the Cisco Data Center AI Infrastructure Exam Cost?
The exam fee is $300 USD. Cisco also allows candidates to use Cisco Learning Credits, which can be an excellent option for organizations or professionals with training budgets earmarked for certification growth.
How Many Questions Are There on the DCAI Exam?
You’ll face 65 questions across multiple domains. The format includes both multiple-choice and multi-select questions, with no case studies, ensuring a focused evaluation of your technical and conceptual knowledge.
What Type of Questions Can You Expect?
Cisco uses a mix of multiple-choice and multi-select questions to assess your abilities. Expect scenario-based prompts about system configuration, network evaluation, or troubleshooting high-performance AI environments. The questions test both theory and applied understanding of Cisco’s AI ecosystem.
What Language Is the Exam Offered In?
The Cisco 300-640 DCAI exam is currently available in English. While English fluency is recommended, the terminology used aligns with Cisco best practices and technical documentation standards, making it accessible for global learners.
What Score Do You Need to Pass?
To pass, you’ll need a score of approximately 825 out of 1000, or about 83%. The score reflects Cisco’s commitment to ensuring certified professionals maintain a deep and practical grasp of AI infrastructure technologies within complex data centers.
How Difficult Is the Cisco DCAI 300-640 Certification Exam?
This exam is designed for professionals experienced with Cisco infrastructure technologies and AI workloads. Candidates should be comfortable with data center operations, networking configurations, and the components that enable high-performance AI workloads. With thoughtful preparation and top-rated Cisco Certified Specialist Data Center AI Infrastructure practice exams, you can build the confidence needed to succeed on your first attempt.
What Are the Core Domains, and How Are They Weighted?
The Cisco DCAI exam blueprint emphasizes four major content areas:
-
AI Fundamentals and Applications (20%)
- AI/ML workloads including RAG, training, inference, and generative models
- AI lifecycle and practical use cases
- Infrastructure types (cloud, hybrid, edge, on-premises)
- Cisco AI solutions including AI PODs, Hyperfabric AI, and AI Canvas
-
AI Infrastructure Components and Architecture (30%)
- Network and compute design based on performance and scalability needs
- Storage optimization for redundancy and throughput
- Power efficiency, sustainability, and hybrid integration
-
AI Infrastructure Deployment and Data Management (30%)
- Network configuration with congestion control and QoS
- Cisco UCS compute and storage configuration
- Deployment using orchestration tools like APIC, Intersight, and Nexus Dashboard
-
AI Infrastructure Operations and Troubleshooting (20%)
- Performance benchmarking and system telemetry
- Monitoring Cisco AI infrastructure reliability and system health
- Troubleshooting using operational data and log correlation
What Knowledge Areas Should You Master Before Taking the Exam?
To fully prepare, focus on understanding how Cisco technologies integrate across AI workloads. Prioritize:
- Network performance optimization with PFC, ECN, and QoS
- Compute and GPU provisioning in Cisco UCS environments
- Storage design for AI scale-out architecture
- Operational telemetry and real-time monitoring via Cisco Intersight and Nexus Dashboard
These areas form the backbone of the modern AI data center infrastructure.
Does This Certification Expire?
Yes. Cisco certifications are generally valid for three years. To maintain your credential, you can retake the exam, pass a higher-level Cisco certification, or earn Continuing Education credits that count toward recertification.
Which Cisco Training Courses Can Help You Prepare?
Cisco recommends the following instructor-led courses to gear up for the exam:
- AI Solutions on Cisco Infrastructure Essentials (DCAIE)
- Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT)
These courses focus on building, operating, and securing AI-focused architectures across Cisco systems.
What Tools and Platforms Should You Be Familiar With?
Candidates should be comfortable with:
- Cisco Nexus Dashboard for infrastructure orchestration
- Cisco Intersight for hybrid cloud management and observability
- Cisco UCS for compute policy control
- Cisco APIC and Hyperfabric AI for fabric automation and workload deployment
Understanding the interplay between these platforms is key to achieving high-performance AI infrastructure.
How Are Cisco DCAI Skills Relevant in the Modern Data Center?
As AI workloads become core to enterprise operations, efficient and scalable infrastructure has never been more important. The Cisco Data Center AI Infrastructure certification validates your ability to design data centers capable of powering advanced analytics, generative AI, and ML pipelines with speed and reliability. These skills are increasingly sought by global organizations investing in digital transformation.
What Experience Level Is Recommended Before Attempting the Exam?
Cisco recommends candidates have intermediate to advanced experience managing data centers or networking systems. Understanding virtualization, fabric management, and AI workload deployment gives you a strong foundation to approach the DCAI certification with confidence.
What Technologies Are Covered in the DCAI Curriculum?
Key technology areas include:
- AI Compute and GPU technologies like NVLink and virtualization
- Data center networking including RDMA, RoCEv2, and congestion control
- Storage optimization using SAN, NVMe, and Fibre Channel
- Automation and orchestration tools for AI workload deployment and scaling
A balanced understanding of each of these technologies ensures readiness for real-world AI data operations.
Are There Prerequisites for Taking the Cisco 300-640 Exam?
There are no mandatory prerequisites. However, familiarity with Cisco data center technologies and AI-enabled networking concepts is highly recommended. The exam also contributes toward the CCNP Data Center concentration requirements, making it a valuable part of a broader Cisco certification journey.
How Can You Register for the Cisco DCAI 300-640 Exam?
Registration is quick and flexible through the Cisco Certification portal or Pearson VUE testing centers. You can choose either an online proctored exam or in-person testing location based on your preference. Ensure your testing environment meets all Cisco exam guidelines before starting.
What Comes After the DCAI Certification?
After earning your Cisco Data Center AI Infrastructure Specialist certification, you might advance toward:
- CCNP Data Center Certification for enterprise infrastructure mastery
- Cisco Specialist in Data Center Core Technologies (DCCOR)
- AI and Machine Learning specialization tracks expanding your design and implementation expertise
Each step builds on your proven knowledge, opening new opportunities across networking, automation, and hybrid infrastructure.
Where Can You Learn More and Register Officially?
For the most accurate details on exam policies, scheduling, and Cisco’s official learning path, visit the official Cisco Certified Specialist Data Center AI Infrastructure exam page.
The Cisco Certified Specialist – Data Center AI Infrastructure exam is a gateway to mastering the infrastructure that powers today’s most advanced AI systems. With solid preparation and hands-on practice, you’ll gain one of the most forward-looking credentials Cisco offers—positioning yourself at the forefront of AI and data center innovation.
