Top 3 tips: (1) Take AI Practitioner first for exam structure familiarity, (2) Focus on Bedrock integrations with other AWS services, (3) Budget the full 4 hours and prepare physically for endurance
Why I Pivoted from Solutions Architect Professional to GenAI (and What Surprised Me)
I recently got my AWS Generative AI Developer Professional certification, along with the early adopter badge. Here’s the thing, this wasn’t my original plan at all.
I’d been preparing for the AWS Solutions Architect Professional for some time, with the exam scheduled somewhere in December. Then AWS launched the Generative AI Developer certification mid-November, and I found out about it at the beginning of December.
I decided to dig into it. With significant AWS experience and training already under my belt, I wanted to understand what this certification actually meant and whether pivoting made strategic sense.
Turns out, it did. But the path wasn’t straightforward.
What I Thought the Exam Would Be vs. What It Really Targets
I understood very quickly that this certification is Bedrock and AI heavy. What I wasn’t sure about was how deep it went into machine learning territory.
I tried my best to find information online. No luck. With a brand-new certification, the community hadn’t built up the usual knowledge base of “here’s what to expect” posts. That’s the early adopter tax—you’re trading uncertainty for the badge before the market gets flooded.
What I eventually discovered through preparation and the exam itself:
Bedrock is the core. If you don’t know Bedrock inside and out, you’re not passing.
Integration is everything. More than 50% of questions require knowledge of how Bedrock works with other AWS services, Lambda for Agents, OpenSearch for RAG, IAM for permissions, CloudWatch for monitoring, Comprehend for PII detection.
SageMaker shows up, but it’s not the focus. There are ML questions, but they’re not as dominant as some practice tests suggest.
This is really a GenAI Architect exam in disguise. Despite the “Developer” title, the focus is on integrating services and designing solutions, not just writing Python code.
The exam aligns with where the industry is heading. According to TechTarget’s analysis of 2026 generative AI trends, agentic AI orchestration and plug-and-play LLMs are becoming key focus areas, exactly what Bedrock enables. AWS is making this certification hard because the market value for these skills is projected to skyrocket.
My Preparation Path: What I Used and Why
Starting with Frank Kane’s Course (The Skim Phase)
Knowing the exam was Bedrock-heavy, I decided to take Frank Kane’s Udemy course to get a grasp of it. The course is substantial, 22 hours of learning. Initially, I just skimmed it.
What I realized: this is all about Bedrock and how Bedrock works together with other AWS services, plus the AI solutions AWS provides. It gave me a mental map of what I needed to know, even if I wasn’t ready to go deep yet.
The AI Practitioner Detour (Strategic, Not a Distraction)
I remembered that the AI Practitioner certification also touches on many AWS AI solutions and Bedrock. So I made a decision that ended up being crucial: take AI Practitioner first.
Not because I needed the knowledge, the requirements are somewhat light, more general understanding of ML/AI terminology than hands-on skills. But because I needed to understand the exam structure and feeling before jumping into a Professional-level certification with zero practice materials available.
I used Stephane Maarek’s practice test. Did it twice before scheduling the exam. The exam itself was very straightforward, and I got results immediately.
This gave me exactly what I needed: training for the AWS Generative AI Professional format without the high stakes.
The Deep Dive (Where Real Learning Happened)
After AI Practitioner, I went back to Frank Kane’s course, this time properly. I started playing around with Bedrock and foundation models hands-on.
After making sure I had a solid understanding, I did the course’s practice test.
It felt very, very easy. Too easy for a Professional certification.
That worried me.
Practice Tests Reality Check: Too Easy, Off-Target, or ML-Heavy
I did some digging around, and many Redditors shared the same experience: the exam is Professional-level hard, but there aren’t many accurate practice tests available. The questions are reportedly brutal.
So I practiced even more with Bedrock directly:
Deployed guardrails for a test project (critical for enterprise adoption—hallucination prevention and PII masking are the biggest blockers right now)
Re-read the entire AWS documentation at least twice
I tried Gemini’s quiz features, but it kept drifting into ML Specialty territory rather than AWS Bedrock Generative AI specifics. Not helpful.
Then I found people recommending a new practice test on Tutorials Dojo. I took it.
Reality check: I was barely getting 65%. And I understood exactly why, I had very little experience with Machine Learning, and many questions were SageMaker ML-heavy.
I worked to get to a 75% rate on the two question sets there. But it felt like I’d need another month or two of deep ML study to score higher.
The SageMaker Dilemma: How Much ML You Actually Need
I was confronted with a big dilemma.
Option A: Take the Generative AI exam without deep SageMaker knowledge. Option B: Spend another month or two mastering ML concepts first.
Many people in the community recommended sticking to Bedrock. The logic: this is a Generative AI certification, not ML Specialty. SageMaker matters, but it’s not the core.
I decided to take my chances.
The result: It went really well.
While the exam did have a couple of SageMaker questions, the majority were Bedrock-heavy. All the hands-on Bedrock practice paid off.
Here’s the lesson: knowing what NOT to study deeply can be as important as knowing what to study. I could have spent two months on SageMaker and still faced the same Bedrock-integration questions. The risk calculation was worth it.
What the Exam Actually Felt Like: Question Style, Time Pressure, and Mental Endurance
The questions were some of the toughest I had ever seen on any AWS exam.
Not because of technical complexity alone, but because of how they were formulated. Each question required building a mental model with two or three layers of assumptions before arriving at an answer.
Here’s what I mean. Don’t expect questions like “What does Guardrails do?” Expect something closer to: “If a Guardrail filters PII, but the Agent is configured to retry on failure, and the Lambda timeout is set to X seconds, what is the user experience when the content policy triggers?”
You’re not just recalling facts. You’re simulating system behavior in your head.
Time breakdown:
Some questions took me 10-15 minutes each
I used 3 hours and 30 minutes of the 4-hour exam
That’s an average of about 3 minutes per question
I sped up on the last questions because I physically couldn’t sit still any longer
The 4-hour allocation for non-native English speakers isn’t generous—it’s necessary. And even native speakers should expect to use most of it.
Physical reality: By hour three, I desperately needed to move around. I couldn’t resist the urge to finish quickly just to stand up. Plan for this. The exam is a mental marathon, but it’s also a physical endurance test.
Key Takeaways: How to Prepare Efficiently (Without Overstudying)
Here’s what I’d tell anyone preparing for this certification:
The exam is genuinely difficult. The tough questions aren’t a rumor. Accept this going in and prepare accordingly.
The 4-hour time limit is real. Non-native speakers get this by default, but everyone needs it. Don’t rush through practice tests, simulate the actual pacing.
Prepare physically. No water breaks, no bathroom breaks, no interruptions for the full duration. Eat well before. Hydrate earlier in the day, not right before.
Practice tests are imperfect. They’ll either train you for things not on the exam or be too soft. Use them for structure familiarity, not content accuracy.
AWS documentation + hands-on practice is the real preparation. Deploy guardrails. Build agents. Integrate Comprehend. Read the docs twice.
Know Bedrock integrations cold. More than half the questions require understanding how Bedrock works with Lambda, OpenSearch, IAM, CloudWatch, S3, and other services.
Take another certification first. AI Practitioner or AWS Solutions Architect gives you exam format experience and foundational knowledge. Prior certification experience is incredibly valuable here.
Who Should Take This Next (and What’s Coming)
If you have solid AWS experience and want to position yourself for the generative AI wave, this certification is worth the effort. The early adopter badge won’t be available forever, and the market demand for these skills is only growing.
My recommendation: Don’t rush it, but don’t over-prepare either. Focus on Bedrock, integrations, and hands-on practice. Accept that some ML questions will show up, but don’t let SageMaker anxiety derail your timeline.
In a follow-up post, I’ll share what I actually learned through this process—the technical knowledge that stuck, and what prior experience helped me the most. Stay tuned.
How I Passed the AWS Generative AI Developer Professional Certification (and Earned the Early Adopter Badge)
TL;DR
Why I Pivoted from Solutions Architect Professional to GenAI (and What Surprised Me)
I recently got my AWS Generative AI Developer Professional certification, along with the early adopter badge. Here’s the thing, this wasn’t my original plan at all.
I’d been preparing for the AWS Solutions Architect Professional for some time, with the exam scheduled somewhere in December. Then AWS launched the Generative AI Developer certification mid-November, and I found out about it at the beginning of December.
It caught me off guard. AWS putting this much emphasis on Generative AI specifically? That was a signal worth paying attention to. The generative AI in software development market is experiencing explosive growth, and AWS clearly wants certified professionals ready to build on their platform.
I decided to dig into it. With significant AWS experience and training already under my belt, I wanted to understand what this certification actually meant and whether pivoting made strategic sense.
Turns out, it did. But the path wasn’t straightforward.
What I Thought the Exam Would Be vs. What It Really Targets
I understood very quickly that this certification is Bedrock and AI heavy. What I wasn’t sure about was how deep it went into machine learning territory.
I tried my best to find information online. No luck. With a brand-new certification, the community hadn’t built up the usual knowledge base of “here’s what to expect” posts. That’s the early adopter tax—you’re trading uncertainty for the badge before the market gets flooded.
What I eventually discovered through preparation and the exam itself:
The exam aligns with where the industry is heading. According to TechTarget’s analysis of 2026 generative AI trends, agentic AI orchestration and plug-and-play LLMs are becoming key focus areas, exactly what Bedrock enables. AWS is making this certification hard because the market value for these skills is projected to skyrocket.
My Preparation Path: What I Used and Why
Starting with Frank Kane’s Course (The Skim Phase)
Knowing the exam was Bedrock-heavy, I decided to take Frank Kane’s Udemy course to get a grasp of it. The course is substantial, 22 hours of learning. Initially, I just skimmed it.
What I realized: this is all about Bedrock and how Bedrock works together with other AWS services, plus the AI solutions AWS provides. It gave me a mental map of what I needed to know, even if I wasn’t ready to go deep yet.
The AI Practitioner Detour (Strategic, Not a Distraction)
I remembered that the AI Practitioner certification also touches on many AWS AI solutions and Bedrock. So I made a decision that ended up being crucial: take AI Practitioner first.
Not because I needed the knowledge, the requirements are somewhat light, more general understanding of ML/AI terminology than hands-on skills. But because I needed to understand the exam structure and feeling before jumping into a Professional-level certification with zero practice materials available.
I used Stephane Maarek’s practice test. Did it twice before scheduling the exam. The exam itself was very straightforward, and I got results immediately.
This gave me exactly what I needed: training for the AWS Generative AI Professional format without the high stakes.
The Deep Dive (Where Real Learning Happened)
After AI Practitioner, I went back to Frank Kane’s course, this time properly. I started playing around with Bedrock and foundation models hands-on.
After making sure I had a solid understanding, I did the course’s practice test.
It felt very, very easy. Too easy for a Professional certification.
That worried me.
Practice Tests Reality Check: Too Easy, Off-Target, or ML-Heavy
I did some digging around, and many Redditors shared the same experience: the exam is Professional-level hard, but there aren’t many accurate practice tests available. The questions are reportedly brutal.
So I practiced even more with Bedrock directly:
I tried Gemini’s quiz features, but it kept drifting into ML Specialty territory rather than AWS Bedrock Generative AI specifics. Not helpful.
Then I found people recommending a new practice test on Tutorials Dojo. I took it.
Reality check: I was barely getting 65%. And I understood exactly why, I had very little experience with Machine Learning, and many questions were SageMaker ML-heavy.
I worked to get to a 75% rate on the two question sets there. But it felt like I’d need another month or two of deep ML study to score higher.
The SageMaker Dilemma: How Much ML You Actually Need
I was confronted with a big dilemma.
Option A: Take the Generative AI exam without deep SageMaker knowledge.
Option B: Spend another month or two mastering ML concepts first.
Many people in the community recommended sticking to Bedrock. The logic: this is a Generative AI certification, not ML Specialty. SageMaker matters, but it’s not the core.
I decided to take my chances.
The result: It went really well.
While the exam did have a couple of SageMaker questions, the majority were Bedrock-heavy. All the hands-on Bedrock practice paid off.
What the Exam Actually Felt Like: Question Style, Time Pressure, and Mental Endurance
The questions were some of the toughest I had ever seen on any AWS exam.
Not because of technical complexity alone, but because of how they were formulated. Each question required building a mental model with two or three layers of assumptions before arriving at an answer.
Here’s what I mean. Don’t expect questions like “What does Guardrails do?” Expect something closer to: “If a Guardrail filters PII, but the Agent is configured to retry on failure, and the Lambda timeout is set to X seconds, what is the user experience when the content policy triggers?”
You’re not just recalling facts. You’re simulating system behavior in your head.
Time breakdown:
The 4-hour allocation for non-native English speakers isn’t generous—it’s necessary. And even native speakers should expect to use most of it.
Physical reality: By hour three, I desperately needed to move around. I couldn’t resist the urge to finish quickly just to stand up. Plan for this. The exam is a mental marathon, but it’s also a physical endurance test.
Key Takeaways: How to Prepare Efficiently (Without Overstudying)
Here’s what I’d tell anyone preparing for this certification:
Who Should Take This Next (and What’s Coming)
If you have solid AWS experience and want to position yourself for the generative AI wave, this certification is worth the effort. The early adopter badge won’t be available forever, and the market demand for these skills is only growing.
My recommendation: Don’t rush it, but don’t over-prepare either. Focus on Bedrock, integrations, and hands-on practice. Accept that some ML questions will show up, but don’t let SageMaker anxiety derail your timeline.
In a follow-up post, I’ll share what I actually learned through this process—the technical knowledge that stuck, and what prior experience helped me the most. Stay tuned.
Further Reading
Dan Gurgui | A4G
AI Architect
Weekly Architecture Insights: architectureforgrowth.com/newsletter
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