- Start with AI-901, AWS AI Practitioner, or Google Generative AI Leader for fundamentals.
- Use AI-300, Google ML Engineer, IBM AI Engineering, TensorFlow, NVIDIA, and Databricks when you want deeper machine learning practice.
- Keep explanations open after each question so the vocabulary starts to stick.
AI and data career hub
AI and data practice hub
Use this track for AI fundamentals, generative AI, machine learning, data platforms, and model-building concepts that show up across modern cert paths.
Exams in this hub
Why this track
- Pick the cert path that matches the role you want
- Answer a daily set before committing to a longer session
- Use explanations to turn misses into actual study notes
How to use the AI and data hub
Use this hub when you want the AI side of the catalog without mixing in every general IT page. Start with fundamentals, then pick a cloud, ML, or data-platform lane.
AI and data practice pages
Every exam in this career hub has a focused daily question set, source-backed explanations, and Pro practice options when you want more reps.
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AI-901
Microsoft Azure AI Fundamentals (AI-901)
Microsoft AI-901 daily practice with weak-domain review and readiness tracking.
AI-300
Operationalizing Machine Learning and Generative AI Solutions (AI-300)
Microsoft AI-300 daily practice with weak-domain review and readiness tracking.
AWS AI Practitioner
AWS Certified AI Practitioner (AIF-C01)
Daily AIF-C01 practice with weak-domain review and readiness tracking.
Google ML Engineer
Professional Machine Learning Engineer
Google Cloud ML practice for framing ML problems, data, modeling, pipelines, deployment, monitoring, and responsible AI.
AWS ML Engineer Associate
AWS Certified Machine Learning Engineer - Associate
AWS ML practice for data prep, model development, deployment, monitoring, responsible AI, and SageMaker workflows.
AI-102
Azure AI Engineer Associate
Azure AI Engineer practice for Foundry, generative AI, agents, vision, language, speech, and knowledge mining.
IBM AI Engineering
IBM AI Engineering Professional Certificate
IBM AI Engineering practice for machine learning, deep learning, neural networks, model evaluation, and applied AI workflows.
TensorFlow Developer
TensorFlow Developer Professional Certificate
TensorFlow practice for neural networks, computer vision, NLP, sequences, model training, and certificate-course readiness.
Databricks ML Associate
Databricks Certified Machine Learning Associate
Databricks ML practice for ML workflows, feature engineering, model training, MLflow, experiment tracking, and deployment concepts.
Google Generative AI Leader
Google Cloud Generative AI Leader
Generative AI Leader practice for AI opportunity, responsible AI, business value, implementation planning, and Google Cloud AI services.
NVIDIA GenAI LLM Associate
NVIDIA-Certified Associate Generative AI LLM
NVIDIA generative AI practice for LLM concepts, training, inference, retrieval, responsible AI, and accelerated AI workflows.
Stanford Machine Learning
Stanford / DeepLearning.AI Machine Learning Specialization
Machine learning specialization practice for supervised learning, unsupervised learning, neural networks, recommender systems, and practical ML advice.