TLDR: It depends…😲
Thanks to Nyla’s Continuous Learning Education Benefit, I had the opportunity to earn two highly respected technical certifications over the last two years: the AWS Solutions Architect Associate (AWS SAA-C03) and the Linux Foundation’s Certified Kubernetes Administrator (LF CKA). I chose these certifications to broaden my technical skills as a seasoned data scientist and help me lead more effectively across engineering teams. I know many technical professionals struggle with choosing the right certification that gives them the most bang for their buck. This post is my attempt to break down what each offers from a data scientist’s perspective.
Topics Covered:
- Format & Difficulty
- Preparation Strategy
- Skills Learned
- Relevance to Data Scientists
- My Final Take & Career Impact
BLUF: The AWS SAA exam tests what you know and the LF CKA exam tests what you can do. The AWS exam is like memorizing a cookbook while the Kubernetes exam is like learning how to operate a commercial kitchen.
SAA-C03 is all multiple choice/multi-select. It tests your ability to choose the best solution based on a scenario. Scenarios focus on designing scalable, secure, and cost-efficient architectures using AWS services. No hands-on implementation is required.
CKA is entirely hands-on. You’re dropped into a real Kubernetes command-line environment and tasked with implementing working solutions. You’ll be frantically flipping through official documentation, writing YAML manifests, configuring clusters, debugging pods all under the stress of time. Just like true platform admin!
AWS Solutions Architect Associate
Resources Used:
Study Strategy:
- 40 hours of study over four days
- I watched Neal’s videos at 2x speed, skipped the hands-on demos, and took five of the six practice exams from Stephane all in succession.
Certified Kubernetes Administrator (CKA)
Resources Used:
- LF Kubernetes Fundamentals Course (LFS258)
- Chad Crowell’s CKA-Exercises GitHub Repository
- Kubernetes Official Docs
Study Strategy:
- 80+ hours of study over 6 weeks
- I watched all the videos multiple times through and completed each of the labs twice.
- I narrowly failed my first attempt, used the practice questions from Chad’s GitHub repo to fix my deficiencies, and passed the second time around.
AWS SAA gave me the vocabulary and architectural patterns to work across AWS-based systems. It gave a broad, high-level knowledge of AWS cloud services including:
- Compute (EC2, Lambda, etc.)
- Storage (S3, EFS, EBS, Glacier, etc.)
- Networking (VPCs, Route 53, CloudFront, Global Accelerator, etc.)
- IAM & Security (Organizations, WAF, Control Tower, etc.)
- Monitoring & Logging (CloudWatch, CloudTrail, OpenSearch, etc.)
- Integration tools (SQS, SNS, API Gateway, VPC Peering, PrivateLink, DMS, etc.)
LF CKA provided me with an applied skillset to configure and manage container orchestration environments. It dove deep into:
- Kubernetes architecture (API server, control plane vs. data plane)
- Cluster provisioning (kubeadm, minikube, docker, calico, etc.)
- Deployments & StatefulSets
- Volumes & persistent storage
- Services, ingress, and DNS
- Logging, monitoring, and security
- YAML customizations
AWS SAA: Most directly relevant
If you’re looking to increase the number of tools in your toolkit, this certification is the right place to start. You’ll learn about the resources that will help you to deploy, monitor, and productionize your workloads. And you’ll improve your ability to communicate with cloud engineers and make smart architectural decisions from day one.
LF CKA: Invaluable for senior, technical roles
Kubernetes knowledge is becoming more relevant to data science workflows especially if you’re leading or collaborating with DevOps, MLOps, or software engineering teams. It helped me understand where my models live after I build them, how the cloud capaiblities that I use fit within the broader software ecosystem, and how to deploy containerized workloads end-to-end. Most importantly, it forced me to code, which, in my opinion, is the second most important skill that a data scientist can leverage when problem solving, right next to the ability to rigorously apply the scientific method.
Important caveat (all data scientists love a caveat): I wouldn’t have passed the AWS exam with my abbreviated study preparation if I hadn’t done CKA certification first. There is a lot of overlap in core cloud concepts and SAA prep served as a refresher in many ways, with an AWS-specific flavor.
Both certifications undoubtedly improved my ability to work across modern software organizations and within multi functional teams. Each boosted my ability to communicate with DevOps engineers, cloud architects, and network engineers as well as understand how their duties integrate with mine to achieve the ultimate goal –deliver high quality software solutions.
So which one should you take? Easy answer — it depends. Neither are perfect if you want to grow a data science-specific skillset. I recommend AWS SAA for a broad familiarization with modern cloud services (learn the cookbook) and the LF CKA to learn how to operate a state-of the-art software orchestration platform (run the commercial kitchen). My bias goes towards LF CKA because it forces to you apply your knowledge and you are graded on your actions, both of which better align with the everyday responsibilities of a data scientist: we are evaluated on what we do, not what we know.
