AWS AI Practitioner Exam Topics, Tips & Practice Exams

Exam basics

This exam measures your ability to apply AI and ML knowledge in the AWS environment. Question types include multiple choice, multiple response, ordering, matching, and case study. Your result appears as a scaled score between 100 and 1000 and the minimum passing score is 700, which is lower than the 720 used on many AWS Professional exams and different from the 750 used on many AWS Specialty exams. The scoring model is compensatory, which means you pass based on your total performance rather than on each section. The exam also includes unscored questions that AWS uses to test potential content for future versions.

Content domains and weights

The content is organized into five domains that each focus on specific knowledge areas. Fundamentals of AI and ML accounts for 20 percent. Fundamentals of generative AI accounts for 24 percent. Applications of foundation models has the largest share at 28 percent. Guidelines for responsible AI represents 14 percent. Security, compliance, and governance for AI solutions makes up the remaining 14 percent. Some concepts overlap with the AWS Cloud Practitioner exam and even the Google Cloud path such as the GCP ML Engineer or Generative AI Leader, but this exam focuses on foundational AI literacy on AWS.

Domain 1: fundamentals of AI and ML

This domain tests your knowledge of core AI terminology and where ML fits in modern solutions.

Key concepts and learning types

You should be able to define AI, ML, deep learning, neural networks, computer vision, NLP, models, algorithms, training and inference, bias, fairness, and large language models. You should differentiate supervised, unsupervised, and reinforcement learning and describe batch and real time inference.

Data types and use cases

You should recognize labeled and unlabeled data, tabular and time series data, images and text, and both structured and unstructured data. You should map common problems to techniques such as regression, classification, and clustering and relate them to services in the AWS AI and ML portfolio.

Lifecycle awareness

You should understand the ML lifecycle at a high level including data collection, EDA, preprocessing, feature engineering, training, tuning, evaluation, deployment, and monitoring. You should know where services such as Amazon SageMaker fit along with higher level options used by developers and architects.

Domain 2: fundamentals of generative AI

This domain measures your ability to reason about foundation models and their capabilities.

Core genAI building blocks

You should understand tokens, embeddings, vectors, prompt engineering, transformer models, multimodal and diffusion models, and the lifecycle of foundation models from pretraining to deployment.

Business use cases and limits

You should identify use cases such as text generation, summarization, conversational agents, translation, search, recommendations, and code generation. You should recognize advantages such as adaptability and speed and acknowledge limits such as hallucinations, nondeterminism, and interpretability concerns, which you may also see discussed in AI Practitioner study guides and multi-cloud resources.

AWS options

You should recognize managed choices such as Amazon Bedrock and Amazon Q and where SageMaker and JumpStart can help teams that also pursue paths like DevOps or data engineering.

Domain 3: applications of foundation models

This domain evaluates your knowledge of selecting, customizing, and integrating foundation models.

Model selection and parameters

You should select pre trained models based on cost, modality, latency, multilingual support, context length, and customization needs. You should understand the impact of inference parameters such as temperature and maximum tokens.

Retrieval augmented generation

You should define RAG and describe when to use knowledge bases and vector stores. You should recognize storage choices that architects often pair with models as covered in solutions architect study materials.

Customization approaches

You should compare in-context learning, fine tuning, continued pretraining, and RAG, and understand the cost and governance tradeoffs. You can contrast this domain with the deeper build focus in the ML Specialty path.

Domain 4: guidelines for responsible AI

This domain focuses on safety, fairness, transparency, and human centered practices.

Responsible AI pillars and tools

You should identify bias, fairness, inclusivity, robustness, safety, and veracity. You should recognize guardrails and oversight patterns and how these themes also appear in security study tracks.

Explainability and transparency

You should understand the value of documentation such as model cards and how explainability trades off with capability in some cases. You should know how human review augments AI outputs, which is also a lesson repeated in Cloud Practitioner resources.

Domain 5: security, compliance, and governance

This domain ensures you understand how AI solutions respect organizational and regulatory controls.

Protecting systems and data

You should apply least privilege with IAM, encryption at rest and in transit, and private connectivity. You should understand data lineage and cataloging and how these practices support audits similar to scenarios seen in DevOps and architecture paths.

Governance and compliance awareness

You should recognize standards and the AWS services that assist with compliance monitoring. You should understand policies, review cadences, and training needs that keep AI use aligned with company values.

Out of scope tasks

You are not expected to code custom algorithms, perform hyperparameter tuning, build ML pipelines from scratch, or conduct detailed statistical analysis. You do not need to design enterprise grade VPC architectures or implement deep DevOps for EMR. You also do not need to integrate with other clouds such as the Google Cloud Platform, although multi cloud awareness can help when you later pursue GCP developer or architect credentials.

How to prepare

Start with the official objectives and build a plan that touches all five domains. You can pair a reputable course with practice exams to gain familiarity with the format and to identify weak spots. Many candidates use the popular Udemy practice exam sets for baseline testing, then focus on AI content with AI Practitioner study guides while cross-referencing service overviews from AWS ML, developer, and solutions architect tracks. If you follow communities like Scrumtuous or authors at mcnz.com, you can pick up exam strategies that transfer well across certifications.

You can also use generative AI as a study partner by asking for explanations of topics and requesting practical walkthroughs. When you are ready, circle back to mock exams to verify readiness. If you intend to continue, the ML Specialty and Solutions Architect Professional paths are natural next steps.

Cameron McKenzie writes about software development, Java, and Spring, and creates training content for YouTube and learning platforms. His coverage spans cloud fundamentals, AI practitioner, and multi-cloud AI and ML topics.