Written by the AgileFever editorial team. Facts checked on November 6, 2026 against the official pages of the organizations named in this post. AgileFever sells training for some of the topics covered here, and we say so wherever it applies. Exam facts below come from the exam owners’ own pages on that date. Week and hour figures are our estimates, not published numbers.
Search for an LLMOps certification and you find a mixed list: cloud exams, short course certificates, and badges from small bodies with no public exam guide. They look alike on a checkout page and differ a lot in what they prove. This post gives you a way to judge one before you pay. It names no course providers and ranks none. It uses the exams owned by the platforms that build and run the tools.
If the term is new to you, start with LLMOps vs MLOps: the complete 2026 guide. Short version: LLMOps is the work of running applications built on large language models in production, and it covers prompts, retrieval, evaluation, guardrails, cost and latency.
What an LLMOps certification can and cannot prove
A certificate proves you passed an assessment. It does not prove you can keep a model-backed application healthy on a bad Tuesday. That is why the question to ask is what the assessment tests, and how. A multiple-choice exam can check that you know a concept. Only a practical task, such as building an evaluation set and catching a regression with it, shows you can do the work.
Also check the age of the content. Models, hosted services and exam versions change within months. AWS, for example, lists the English version of its older ML engineer associate exam as available only until September 28, 2026, with an updated version in beta. Microsoft’s Learn page says its Azure AI Engineer Associate certification and renewal assessment are retired. Look at the exam date and version on any LLMOps certification before you study for it.
The syllabus checklist for any LLMOps certification
Use this table as a filter. For each skill, the right column says how a course or exam should test it. If a provider’s outline lacks a skill, or tests it only by asking you to define terms, mark it down.
| Skill | What it means in production | How a course should test it |
|---|---|---|
| Prompt and model versioning | Every prompt, model name, parameter set and retrieval configuration has a version you can roll back to | Give the learner a failing release and ask them to restore the last good version and show the diff |
| Evaluation sets and regression tests | A fixed set of questions with expected answers, scored the same way before and after every change | Ask the learner to build a set of 30 or more cases, run it on two prompt versions and decide which to ship |
| Retrieval pipeline monitoring | You can see which passages were retrieved, whether they were relevant and when the index went stale | Plant a stale document in an index and ask the learner to find it from logs |
| Cost and latency tracking | Tokens, cost per request and response time are logged per feature, with alerts | Ask for cost per 1,000 requests from real logs, then a change that lowers it without lowering the eval score |
| Guardrails | Input and output checks for prompt injection, personal data, unsafe content and off-topic answers | Run a set of hostile inputs against the learner’s application and mark what gets through |
| Deployment patterns | Staged rollout, canary release, fallback to a second model, rollback | Ask the learner to ship a prompt change to 10% of traffic and write the rollback rule |
| Observability | Traces that follow one request through retrieval, model call and tools, plus quality scores over time | Give a bad answer and ask the learner to trace it to the step that caused it |
| Incident response | A runbook for provider outages, quality drops, cost spikes and leaked data | A timed scenario: cost triples overnight, find the cause and write the first three actions |
Evaluation, monitoring, cost and guardrails are the rows that separate production work from a demo. A course that is thin on those and heavy on tool tours will not prepare you for production work.
Which official exams touch LLMOps topics
These are owner-issued exams, checked on November 6, 2026. None is called LLMOps. Each covers part of the checklist above, so treat them as partial proof. Fees and rules change, so confirm them on the owner’s page before you book.
| Exam and owner | Length and format | Fee | Validity | Where it overlaps LLMOps |
|---|---|---|---|---|
| Generative AI Engineer Associate, Databricks | 90 minutes, 45 scored multiple-choice questions | $200 | 2 years | Sections for assembling and deploying apps (22%), evaluation and monitoring (12%) and governance (8%); also design (14%), data preparation (14%) and application development (30%) |
| Professional Machine Learning Engineer, Google Cloud | 2 hours, 50 to 60 multiple-choice and multiple-select questions | $200 plus tax where it applies | Renewal rules in Google’s renewal FAQ | Serving and scaling models, automating pipelines, monitoring AI solutions, and evaluating generative AI solutions |
| Machine Learning Engineer Associate (MLA-C02, beta), AWS | 170 minutes in the beta | $75 during the beta | 3 years | Domains cover deployment and orchestration (24%) and operating, monitoring and securing AI solutions (24%), with agentic workflow observability and cost |
Source: the owners’ certification pages, November 6, 2026. The AWS fee is beta pricing and general availability dates are not yet set. Google lists the Professional Machine Learning Engineer renewal window in its renewal FAQ rather than on the exam page.
These exams are tied to one platform each. A Databricks or AWS exam teaches that platform’s way of doing the work. Principles carry across; button names do not. If your company runs on a different stack, weigh that before you pay.
Credential versus portfolio
An exam answers the question: does this person know the material? A portfolio answers a different one: can this person do the work? Most hiring managers want both, and when only one is possible for an LLMOps certification, the second is harder to fake.
| Question | Credential | Portfolio |
|---|---|---|
| What it proves | You met a standard on a set date | You built something and measured it |
| Cost | Exam fee plus preparation time | Your time, plus model API and cloud usage |
| Goes out of date | Yes, with exam versions and validity periods | Yes, if the tools change, so keep it updated |
| Easy to compare across people | Yes, same exam for all | No, each project differs |
| Easy to check | Owner’s verification page | A reviewer must read the code and the results |
| Shows incident and cost thinking | Rarely | Yes, if you include logs, a runbook and a cost line |
| Best for | Getting past a screen that asks for a named credential | Interviews where you must explain a decision |
Do not choose between them. Pick the exam that matches your employer’s stack, and build one project that covers the checklist. The exam gets you through the filter. The project gets you through the conversation.
Build proof of skill in 8 weeks
This plan is ours, written for an engineer who already calls a model API and wants to show LLMOps skill. It is illustrative, not a published curriculum. Each week ends with an artifact you can put in a repository.
Move prompts and model settings into files under version control. Tag a release. Practise a rollback.
Write 30 to 50 cases with expected answers. Add a script that scores them and prints one number.
Run the evaluation on every change. Fail the build if the score drops below your threshold.
Log retrieved passages and relevance for each question. Find a stale or missing document from the logs.
Log tokens, cost and response time per request. Cut cost per 1,000 requests without losing evaluation score.
Add input and output checks. Test 20 hostile inputs and record what gets through.
Release to a slice of traffic, add traces from request to answer, and write the rollback rule.
Run a cost-spike or outage drill. Write a runbook and a one-page summary with your numbers.
Illustrative plan, our estimate. Total 48 hours.
Evaluation gets the most time because every other skill depends on it. You cannot say a guardrail helps, or a cheaper model is good enough, without a score to compare. If you also want a taught route, free LLM courses in 2026 lists a free order of study, and our LLM certification guide covers the model-side credentials.
Questions to ask any provider before you pay
Use these with every course or credential, including ours. A provider that cannot answer them in writing has told you something.
- Is there a published outline with the skills tested and their weights?
- Which of the eight checklist skills are assessed by a practical task, and which only by questions?
- What does the assessment use as an evaluation set, and can I see a sample task?
- Which model versions and tool versions does the material use, and when was it last updated?
- Who owns the credential, and is there a public page where an employer can verify it?
- How long is it valid, and what does renewal cost?
- What is the full cost, including exam fee, retakes and the cloud or model API usage the labs need?
- What happens if I fail, and how many attempts are included?
- Will I get feedback on my work from a person, or only an automated score?
- Does the certificate come from the vendor that owns the platform, or from the training company?
LLMOps vs MLOps: why the credential question differs
MLOps manages models you train. LLMOps manages applications built on models you usually rent. That changes what you test. The full comparison is in our LLMOps vs MLOps guide. The short version:
| Area | MLOps | LLMOps |
|---|---|---|
| What you version | Data, code and trained model | Prompts, retrieval settings, model name and documents |
| How you test | Accuracy and error metrics on a test set | Evaluation sets with rubric or model-graded scoring, plus human review |
| What breaks | Data drift and model decay | Hallucination, prompt injection, stale documents, cost and latency growth |
| Typical certificate coverage | Well covered by cloud ML exams | Partly covered, spread across several exams |
MLOps knowledge transfers, which is why most people add LLMOps to an existing MLOps base instead of starting from zero. The same is true of the exams: the cloud ML engineer exams above test the MLOps base and add generative AI on top.
Where AgileFever fits
We sell training in this area, so weigh this section accordingly. Our MLOps, LLMOps and Agentic AI Ops BootCamp is 60 live hours over 6 teaching weeks, 100% live online, with 17 modules. It is mainly an MLOps program: LLMOps and Agentic AI Ops are two of the 17 modules, about 12% of the syllabus. It has no exam. You receive a certificate on completing all modules and the capstone, and it is not preparation for the vendor exams listed above. If your goal is a named LLMOps certification, that is a poor match. If your goal is the production base that LLMOps builds on, it fits.
As listed on November 6, 2026, the price is $2,000 in the United States and ₹75,000 in India. Rupee prices are before 18% GST, which is added at checkout for buyers in India. Listed cohorts include November 23, 2026 to January 18, 2027 (US) and November 24, 2026 to January 19, 2027 (India), then December 12, 2026 to January 30, 2027.
Additional discounts are available. Contact us to know more.
For detail, read the module-by-module curriculum, the LLMOps and AgenticOps skills you build in it, the complete guide and the cost breakdown.
Questions people ask
Is there an official LLMOps certification?
Not one exam from a single owner that employers treat as the standard. Cloud and data platforms publish exams that cover parts of it, such as deployment, evaluation, monitoring and governance. Several training companies issue their own LLMOps certificates. Check who owns the credential and whether the outline is public.
What should an LLMOps course cover?
Eight skills: prompt and model versioning, evaluation sets and regression tests, retrieval pipeline monitoring, cost and latency tracking, guardrails, deployment patterns, observability and incident response. Each should be assessed with a practical task. A syllabus that lists tools but no evaluation or monitoring work is incomplete.
Do I need MLOps before LLMOps?
Not strictly, but it helps. Versioning, CI/CD, monitoring and deployment habits carry over. If you already ship software, you can start with LLMOps and fill MLOps gaps as they appear. If you have never deployed anything, build one simple service first.
How long does it take to prepare?
On our estimate, about 48 hours over 8 weeks, or 6 hours a week, to build a portfolio project that covers the checklist. Exam preparation adds time and depends on the exam and your platform experience. Treat the figure as a planning guide, not a promise.
Is a certificate or a portfolio better for getting hired?
Neither guarantees a job. A certificate helps get past a screen that asks for a named credential. A portfolio helps in interviews because you can explain real decisions and numbers. Most candidates do best with one exam that fits the employer’s stack and one measured project.
How much does an LLMOps certification cost?
It depends on the owner. The cloud and data platform exams checked here list fees of $75 (AWS beta) to $200, with validity of 2 to 3 years where stated. Training company certificates vary and may include course fees. Add retakes and the usage cost of model APIs for labs.
