PL-300, Google Data Analytics, AWS and Azure AI: which to take
In this article (5 sections)
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Take a certification only when it tests the tools named in the jobs you are applying for. For Power BI analyst roles, Microsoft's PL-300 is the most directly relevant; the Google Data Analytics certificate suits complete beginners who want a structured introduction; AWS AIF-C01 and Azure AI-901 are foundational AI exams for people who will use or explain AI services, not build models. Details below were checked on the official pages in September 2026 — confirm them again before you pay.
What each one actually is
| Credential | Issuer and format | What it tests | Validity |
|---|---|---|---|
| Microsoft Certified: Power BI Data Analyst Associate (exam PL-300) | Microsoft; proctored exam, 100 minutes | Prepare, model, visualise and analyse data; manage and secure Power BI; Power Query and DAX | Renew every 12 months through a free online assessment |
| Google Data Analytics Professional Certificate | Google, on Coursera; self-paced course series, not a single exam | Spreadsheets, SQL, Tableau, a Python course, a capstone case study | Check the course page |
| AWS Certified AI Practitioner (exam AIF-C01) | AWS; 65 questions (50 scored), 90 minutes, pass at 700 on a 100–1,000 scale | AI/ML and generative AI fundamentals, foundation-model use, responsible AI, security and governance | 3 years |
| Microsoft Certified: Azure AI Fundamentals (exam AI-901) | Microsoft; proctored exam | AI concepts and implementing AI solutions with Microsoft Foundry | Check the certification page |
Sources: PL-300 certification page, Google Data Analytics on Coursera, AWS AIF-C01 exam guide, Azure AI Fundamentals page.
Two details worth knowing. The Azure AI Fundamentals page now names AI-901 as its required exam, and notes the English version was updated on 15 April 2026 — older study material for AI-900 may not match. And the Google certificate's Coursera page, as of September 2026, lists nine courses including a Python course, while its FAQ still describes R; read the current syllabus rather than a blog summary of it.
Match the credential to the role you want
Start from ten job postings for your target role and count which tools they name. Then use this table.
| If most postings ask for… | Consider | Skip for now |
|---|---|---|
| Power BI, DAX, Excel | PL-300 | AWS or Azure AI fundamentals |
| "SQL, Excel, any BI tool" with no specific vendor | Google Data Analytics, if you want structure; otherwise build projects | PL-300 until you see Power BI named |
| Tableau | A Tableau-focused project; Google's certificate covers some Tableau | PL-300 |
| AI tools in a business or pre-sales role | AWS AIF-C01 or Azure AI-901, matching the cloud the employer uses | Advanced ML certifications |
| Python, machine learning, model building | Portfolio projects first; no certificate here substitutes for them | Foundational AI exams, which exclude model building |
The last row is important. The AWS exam guide explicitly lists developing models, tuning hyperparameters and building ML pipelines as out of scope for AIF-C01. It will not show a hiring manager that you can do data science.
A worth-it score before you book
Score each question 0, 1 or 2 and add them up.
- 1Named in postings. How many of your ten sample postings name this exact tool or credential? (0 = none, 1 = two or three, 2 = four or more)
- 2Hands-on match. Does preparing for it make you build something you can show? (0 = only reading, 1 = some labs, 2 = a real report or project)
- 3Gap it fills. Does it cover a tool your resume does not already prove? (0 = no, 1 = partly, 2 = yes)
- 4Cost of your time. Can you prepare without pausing your portfolio work? (0 = no, 1 = partly, 2 = yes)
- 5Upkeep. Can you keep it current? PL-300 needs an annual renewal assessment. (0 = unlikely, 2 = yes)
A score of 7 or more suggests the exam is worth booking. Under 5, your hours are probably better spent on a portfolio project; see certificates versus portfolio for how to split time.
Preparing without wasting money
- Use the free official material first. Microsoft Learn has free Power BI training paths and a PL-300 practice assessment; AWS publishes the exam guide with domain weightings (fundamentals of generative AI and applications of foundation models together make up just over half of the scored content).
- Book the exam only after passing a practice test comfortably. Microsoft's page notes you can retake after 24 hours for a first failure, but each attempt is paid.
- Register with a personal account, not a work or college one. Microsoft warns that exam records tied to an organisational account can be lost when you leave.
- Put the credential ID on your resume so an employer can verify it, rather than just the logo.
Where a course fits
A structured programme and a certification do different jobs: the programme builds skills through projects and feedback, the certification is an external check on one vendor's tool. If Power BI is central to your target roles, the Data Analyst programme covers Power BI alongside SQL and Excel, and preparing for PL-300 afterwards uses the same material. Run your current resume through the free skill check to see which tool gaps a certification would actually close.
Frequently asked questions
Is PL-300 worth it for a fresher data analyst?
It is worth it if the jobs you are applying for name Power BI, because the exam tests the Power Query, modelling, DAX and security work those roles involve. It is less useful if your target roles are Tableau- or Python-heavy. Build at least one Power BI portfolio report first, since the exam assumes hands-on familiarity.
Has Azure AI-900 been replaced?
Microsoft's Azure AI Fundamentals certification page now lists exam AI-901 as the required exam, with the English version updated on 15 April 2026. Check the official certification page before booking, because exam codes and content change.
Does the AWS AI Practitioner exam require coding?
No. AWS's exam guide for AIF-C01 describes a candidate who uses but does not necessarily build AI solutions, and lists developing models, feature engineering and building pipelines as out of scope. It tests understanding of AI, generative AI, responsible AI and AWS services.
Pankit Kumar has 10 years in Data Science & AI, building and shipping production systems in regulated pharma and clinical environments. He is a freelance trainer at Boston Institute of Analytics, AnalytixLabs and Scaler, and has taught this material to thousands of working professionals.
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