Last updated on October 15, 2024
The AWS AI Practitioner – AIF-C01 Certification exam is one of the newest certifications of Amazon Web Services. This exam is intended for individuals who can effectively demonstrate an overall knowledge of AI/ML, generative AI technologies, and associated AWS services and tools independent of a specific job role. The target candidate for this certification should have a solid understanding with AI, ML, and generative AI concepts, and can also determine the right AI/ML technologies for specific use cases while applying these technologies responsibly.
Additionally, you should have up to 6 months of experience working with AI/ML technologies on AWS. Aside from that, familiarity on the AWS Cloud and its services are needed. This includes the following:
- Core AWS services such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker, along with their operational applications
- AWS shared responsibility model for security and compliance
- AWS Identity and Access Management (AWS IAM) for managing security and access to AWS resources
- AWS global infrastructure, including the concepts of AWS Regions, Availability Zones, and edge locations
- AWS services pricing models.
It should be pointed out that the focus here is the “familiarity” of the candidate with these technologies, not their mastery. As long as you already have an experience on using these tools and technologies especially with the integration of the AWS Cloud and services, then this certification is highly recommended for you!
NEW Question types for the AWS Certification Exams!
Last July, the AWS announced that their Certification exams are adding three new question types which are: ordering, matching, and case study. These new question types were included to reduce your reading time while covering more key concepts. Ordering and matching questions are a more efficient method for assessing procedural understanding and pairing skills compared to multiple-choice or multiple-response questions. Meanwhile, case studies allow multiple questions to be asked based on one scenario, so you won’t need to read a new scenario for every question. These new question types will carry the same point value as multiple-choice and multiple-response questions, and they will be integrated throughout the exam alongside the existing question formats. And these new question types will first be featured on this new AWS Certified AI Practitioner exam (along with the AWS Certified Machine Learning Engineer-Associate exam). That’s why candidates should now adjust their preparation strategies by familiarizing yourself with these new question formats, emphasizing on learning the sequences or processes related to AWS services, and improving more your critical thinking and analysis. This will validate how you can apply your knowledge to develop effective solutions to real-life scenarios and problems.
Even with the addition of new question types, there’s no need to worry as it won’t lead to considerable changes in the exams since the total number of exam questions and allotted time for taking the exam still stays the same. The AIF-C01 exam includes 65 questions and your exam results are presented as a scaled score ranging from 100 to 1,000, with a minimum passing score set at 700.
AWS Certified AI Practitioner AIF-C01 Exam Domains
The official exam guide for the AWS Certified AI Practitioner AIF-C01 provides a comprehensive list of exam domains, relevant topics, and services that require your focus. The certification exam comprises of five (5) exam domains and their respective weightings, as shown below:
| AIF-C01 Exam Domains: | Percentage of Exam (%) |
| Domain 1: Fundamentals of AI and ML | 20% |
| Domain 2: Fundamentals of Generative AI | 24% |
| Domain 3: Applications of Foundational Models | 28% |
| Domain 4: Guidelines for Responsible AI | 14% |
| Domain 5: Security, Compliance, and Governance for AI Solutions | 14% |
| Total: | 100% |
Since the third domain which is “Applications of Foundational Models” holds the highest exam coverage of 28%, you should give importance to the topics included in this section. However, it’s equally important to devote sufficient attention to the other domains, as they also contribute significantly to your overall understanding and performance on the exam. Each domain plays a crucial role in your preparation, and neglecting them could leave gaps in your knowledge. Listed below are the exam domains and their respective skills and knowledge that you should posses.
AIF-C01 Exam Domain 1: Fundamentals of AI and ML
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1.1: Explain basic AI concepts and terminologies
This section covers the fundamental concepts in artificial intelligence (AI), defining key terms such as AI, machine learning (ML), deep learning, neural networks, and natural language processing (NLP). It explains how AI, ML, and deep learning are related and how they differ from each other. Additionally, it covers various types of inferencing, such as batch and real-time, and describes the different data types used in AI models, including labeled and unlabeled data, as well as structured and unstructured data (which includes text and images). Lastly, it introduces the three main learning methods in AI: supervised learning, unsupervised learning, and reinforcement learning.
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1.2: Identify practical use cases for AI
This section focuses on how AI and machine learning (ML) can be beneficial in various applications, such as improving human decision-making, scaling solutions, and automating processes. It also discusses when AI/ML might not be the best choice, such as when a specific result is needed rather than a prediction or when cost-effectiveness needs to be considered. Furthermore, it discusses the appropriate ML techniques for different scenarios, including regression, classification, and clustering. The section provides real-world examples of AI applications, such as computer vision, natural language processing (NLP), speech recognition, recommendation systems, fraud detection, and forecasting. Lastly, it explains the features of AWS managed AI/ML services like Amazon SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, and Amazon Polly.
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1.3: Describe the ML development lifecycle
This section explains the main parts of a machine learning (ML) pipeline, including steps like collecting data, analyzing it, preparing it for modeling, training the model, tuning its settings, evaluating its performance, deploying it, and monitoring it afterward. It explains where to find ML models, including pre-trained models available for free and custom models made for specific tasks. The section also covers how to use a model in real-world applications, whether through a managed API service or a self-hosted API. It highlights useful AWS tools for each stage of the ML pipeline, like SageMaker and its built-in features. Additionally, it introduces key concepts of ML operations (MLOps), such as repeatable processes and model monitoring. Finally, it talks about how to measure the performance of ML models using metrics like accuracy and F1 score, as well as business metrics like cost per user and return on investment (ROI).
AIF-C01 Exam Domain 2: Fundamentals of Generative AI
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2.1: Explain the basic concepts of generative AI.
This section explains the basic ideas behind generative AI, including terms like tokens, embeddings, and prompt engineering. It discusses various uses for generative AI, such as creating images, videos, audio, summarizing text, building chatbots, translating languages, generating code, acting as customer service agents, and enhancing search and recommendations. Lastly, it covers the steps involved in the lifecycle of foundation models, such as choosing data and training the model.
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2.2: Understand the capabilities and limitations of generative AI for solving business problems
In this section, we explore the perks of generative AI, such as its ability to adapt quickly, respond in real-time, and be user-friendly. However, it’s important to recognize some of its downsides too, like the risk of “hallucinations” (when the AI makes stuff up), challenges in understanding its outputs, and occasional inaccuracies. When deciding on the right generative AI models to use, there are several factors to keep in mind, including the type of model, how well it performs, and any compliance requirements you may have. Finally, we’ll look at how to measure the business value of generative AI applications, focusing on key metrics like efficiency, accuracy, and customer lifetime value.
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2.3: Describe AWS infrastructure and technologies for building generative AI applications
This section covers the AWS tools available for building generative AI applications, including Amazon SageMaker JumpStart, Amazon Bedrock, PartyRock (an Amazon Bedrock Playground), and Amazon Q. We’ll discuss why using AWS for generative AI is a smart choice, highlighting benefits like easy access, lower barriers to getting started, efficiency, cost savings, faster time to market, and the ability to achieve your business goals. Plus, we’ll see how AWS’s infrastructure helps keep your applications secure and compliant while ensuring responsibility and safety. Finally, we’ll take a closer look at the costs associated with AWS generative AI services, considering aspects like responsiveness, availability, performance, regional options, token-based pricing, and custom models.
AIF-C01 Exam Domain 3: Application of Foundational Models
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3.1: Describe design considerations for applications that use foundation models
This section highlights what to consider when picking pre-trained models. Key factors include how much they cost, what types of data they work with, how fast they respond, whether they support multiple languages, their size and complexity, how customizable they are, and the input and output lengths. We’ll also explain how things like temperature and input/output length can affect the model’s responses. Plus, we’ll dive into Retrieval Augmented Generation (RAG) and its business uses, particularly with tools like Amazon Bedrock and knowledge bases. We’ll also mention AWS services that help store embeddings in vector databases, such as Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB (which works with MongoDB), and Amazon RDS for PostgreSQL. Lastly, we’ll discuss the costs associated with customizing foundation models through various methods, such as pre-training, fine-tuning, in-context learning, and RAG, as well as how agents help manage multi-step tasks in Amazon Bedrock.
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3.2: Choose effective prompt engineering techniques
In this section, we’ll explore the key ideas and elements of prompt engineering. This includes understanding the importance of context, how to give clear instructions, using negative prompts, and navigating the model’s latent space. We’ll also cover various techniques for effective prompt engineering, such as chain-of-thought, zero-shot, single-shot, few-shot prompts, and prompt templates. Additionally, we’ll discuss the advantages and best practices for prompt engineering, including improving response quality, the importance of experimentation, setting guardrails, discovering new possibilities, and the value of being specific and concise—sometimes even using multiple comments. Finally, we’ll look at the potential risks and limitations associated with prompt engineering, like exposure to harmful content, the risk of poisoning the model, hijacking it, or jailbreaking.
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3.3: Describe the training and fine-tuning process for foundation models
This section explains how to train a foundation model, starting with pre-training, where the model learns from a large dataset, followed by fine-tuning to improve its performance on specific tasks. Continuous pre-training keeps the model updated over time. Fine-tuning methods include instruction tuning (training the model to follow commands), adapting it to particular areas, and using transfer learning to apply knowledge from one model to another. Preparing the data for fine-tuning involves organizing it well, ensuring it’s large enough and accurately labeled, and making it representative of real-world situations. Overall, these steps help create a model that performs effectively in practical applications.
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3.4: Describe methods to evaluate foundation model performance
This section covers how to evaluate the performance of foundation models. It starts with approaches like human evaluation and using benchmark datasets to see how well the model works. We’ll also look at important metrics for assessment, such as ROUGE (for summarization), BLEU (for translation), and BERTScore (for comparing text). Lastly, we’ll discuss how to determine if the model successfully meets business goals, focusing on factors like productivity, user engagement, and task performance.
AIF-C01 Exam Domain 4: Guidelines for Responsible AI
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4.1: Explain the development of AI systems that are responsible
This section highlights the key aspects of responsible AI, such as fairness, inclusivity, safety, and accuracy. It explains how to use tools like Guardrails for Amazon Bedrock to identify these features and emphasizes the importance of selecting models with environmental sustainability in mind. We’ll also discuss potential legal risks of generative AI, including intellectual property issues and biased outputs that could damage customer trust. The importance of using inclusive and diverse datasets is covered, along with how bias and variance can lead to inaccuracies. Finally, we’ll look at tools for detecting and monitoring bias, such as Amazon SageMaker Clarify and Model Monitor.
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4.2: Recognize the importance of transparent and explainable models
This section explains the differences between transparent, explainable models and those that are not. It discusses tools like Amazon SageMaker Model Cards that help identify which models are clear and understandable. We’ll also look at the tradeoffs between model safety and transparency, balancing how interpretable a model is against its performance. Lastly, we’ll cover the principles of human-centered design that ensure AI is explainable and user-friendly.
AIF-C01 Exam Domain 5: Security, Compliance, and Governance for AI Solutions
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5.1: Explain methods to secure AI systems
This section focuses on securing AI systems using AWS tools like IAM roles, encryption, and Amazon Macie, while also understanding the shared responsibility model. It highlights the importance of citing sources and tracking data origins with concepts like data lineage and SageMaker Model Cards. We’ll share best practices for secure data management, such as ensuring data quality and controlling access to information. Lastly, we’ll discuss key security and privacy issues for AI systems, including protecting applications, detecting threats, managing vulnerabilities, and keeping data encrypted both when it’s stored and when it’s in transit.
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5.2: Recognize governance and compliance regulations for AI systems
This section highlights the key regulatory standards for AI systems (such as ISO and SOC) and discusses AWS tools that help ensure compliance, like AWS Config and Amazon Inspector. We’ll explore data governance strategies, focusing on how to manage data throughout its lifecycle, monitor it, and retain it properly. Lastly, we’ll cover the steps needed to follow governance protocols, including creating policies, scheduling regular reviews, using frameworks like the Generative AI Security Scoping Matrix, maintaining transparency, and training your team effectively.
What AWS services are included in the AIF-C01 Exam?
The AWS Certified AI Practitioner – AIF-C01 Exam Guide provides a breakdown of the exam domains and a comprehensive list of important tools, technologies, and concepts covered in the AIF-C01 exam. Below is a non-exhaustive list of AWS services and features that should be studied for the exam based on the information provided in the official exam guide. It’s important to remember that this list is subject to change, but it can still be useful in identifying the AWS services that require more attention.
In-scope AWS services and features
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Analytics:
Compute: Containers:
Cost Management:
Database:
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Machine Learning:
Management and Governance: Networking and Content Delivery: Security, Identity, and Compliance: |
Exam Prep Materials for the AIF-C01 AWS Certification Exam
To have a better understanding on those AWS services stated above, you can enroll in different free or premium digital courses that we recommend to help you fill any gaps in your knowledge through hands-on experience and comprehensive resources:
Free AWS ML Digital Courses
- Digital Courses for Machine Learning available in the Tutorials Dojo Portal (in collaboration with AWS)
- Courses from AWS SkillBuilder site which includes:
- Standard Exam Prep Plan – only free resources; and
- Enhanced Exam Prep Plan – includes additional content for AWS SkillBuilder subscribers, such as AWS Builder Labs, game-based learning, Official Pretests, and more exam-style questions
To know more about the AIF-C01 exam itself, you can always visit the official AWS Certification page for the AWS Certified AI Practitioner (AIF-C01). This page provides the most up-to-date information, including the link to schedule your AIF-C01 exam and access to the official Exam Guide.
Andrew Brown’s Free AIF-C01 Course on FreeCodeCamp YouTube
Another awesome resource that we recommend for your AWS Certified AI Practitioner exam preparation is Andrew Brown’s AIF-C01 course on FreeCodeCamp YouTube. Andrew Brown is the CEO of ExamPro and also an AWS Community Hero as well. Check out his informative 15-hour FREE AIF-C01 course here:
Validate your knowledge for the AIF-C01 AWS Certified AI Practitioner Exam
After reviewing the concepts of AI/ML and gaining hands-on experience with the tools and technologies using AWS services, you should now be ready to take the practice exams as a way to gauge your learnings for the real exam. While AWS doesn’t have a sample practice test for free, you can check out our official AIF-C01 sampler. You can also opt to buy the longer AWS sample practice test at aws.training, and use the discount coupon you received from any previously taken certification exams.
But of course, these sample practice tests do not mimic the difficulty of the real AI Practitioner exam. That is why we highly encourage using other mock exams such as our very own AWS Certified AI Practitioner Practice Exam course which contains high-quality questions with complete explanations on correct and incorrect answers, visual images and diagrams, YouTube videos as needed, and also contains reference links to official AWS documentation as well as our cheat sheets and study guides. Stay tuned, as the AWS Certified AI Practitioner Exam Study Guide eBook will be coming soon!
Sample Practice Test Questions for AIF-C01:
Question 1:
An e-commerce company receives hundreds of invoices from suppliers every day. The finance team spends a significant amount of time manually extracting relevant information from these invoices, such as invoice numbers, line items, and total amounts. The goal is to streamline this process using AI-powered tools.
Which of the following options will meet the requirements?
- Fraud detection
- Intelligent Document Processing (IDP)
- Computer vision
- Natural language Processing
Correct Answer: 2
Intelligent Document Processing (IDP) automates data processing using OCR, computer vision, NLP, and machine learning. It extracts, categorizes, and generates insights from unstructured data. IDP enhances customer satisfaction and operational efficiency through generative AI-powered automation. Its ready-to-use APIs efficiently process unstructured data at scale, extract critical information, and generate insightful summaries and reports.
Intelligent Document Processing (IDP) involves automating the process of manually entering data from paper-based documents or document images to integrate with other digital business processes. For example, let’s consider a business process workflow that automatically issues orders to suppliers when stock levels are low. Although the process is automated, no order is shipped until the supplier receives payment. The supplier sends an invoice via email, and the accounts team manually enters the data before completing payment, creating manual checkpoints that can lead to bottlenecks or errors. Instead, IDP systems automatically extract invoice data and enter it in the required format in the accounting system. You can use document processing to automate document management with the help of machine learning (ML) and various artificial intelligence (AI) technologies.
Hence, the correct answer is: Intelligent Document Processing (IDP).
The option that says: Computer vision is incorrect because this primarily deals with understanding and interpreting visual information from images or videos. While it can be used for tasks like object detection and image classification, it doesn’t specifically focus on structured data extraction from documents like invoices.
The option that says: Natural Language Processing is incorrect. While this is essential for understanding and processing human language, it doesn’t directly address the structured data extraction requirements from invoices. NLP techniques focus on tasks like sentiment analysis, chatbots, and language translation, which are different from the e-commerce company’s specific needs.
The option that says: Fraud Detection is incorrect because it only focuses on identifying fraudulent activities or patterns rather than document processing.
References:
https://aws.amazon.com/what-is/intelligent-document-processing/
https://aws.amazon.com/machine-learning/ml-use-cases/document-processing/
https://aws.amazon.com/machine-learning/ml-use-cases/document-processing/fintech/
Check out this AWS Machine Learning and AI Cheat Sheets:
https://tutorialsdojo.com/aws-cheat-sheets-aws-machine-learning-and-ai/
Question 2:
A five-star hotel has accumulated a significant volume of customer reviews and feedback forms. The hotel intends to collect these reviews to enhance its services and highlight recurring issues or concerns raised by their guests. They are also interested in analyzing feedback to introduce new amenities, although their primary focus remains improving current services and addressing frequent complaints.
Which AWS service should the hotel use to effectively analyze customer feedback and enhance its services?
- Amazon Bedrock
- Amazon Kendra
- Amazon QuickSight
- Amazon Comprehend
Correct Answer: 4
Amazon Comprehend is a natural language processing (NLP) service that utilizes machine learning to find insights and relationships in a text. It can identify the language of the text, extract key phrases, places, people, brands, or events, understand how positive or negative the text is, analyze text using tokenization and parts of speech, and automatically organize a collection of text files by topic.
Amazon Comprehend would be the best fit for analyzing customer reviews and feedback forms to extract insights, identify common issues, and understand sentiment. This helps pinpoint areas for improvement and enhance overall service quality.
Hence, the correct answer is: Amazon Comprehend.
The option that says: Amazon Bedrock is incorrect because this service simply makes foundational models available via an API, allowing developers to build and scale generative AI applications. It is not specifically designed for extracting insights from customer feedback.
The option that says: Amazon Kendra is incorrect because it is just an intelligent search service powered by machine learning. While it can help in searching and indexing data, it does not provide the sentiment analysis or insight extraction capabilities required for this scenario.
The option that says: Amazon QuickSight is incorrect because this service is primarily used to create visual dashboards from structured data and lacks the natural language processing capabilities needed to analyze textual feedback. It is not relevant to the requirement of analyzing customer feedback to extract insights and sentiment.
References:
https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html
https://docs.aws.amazon.com/comprehend/latest/dg/getting-started.html
Check out this Amazon Comprehend Cheat Sheet:
Click here for more AWS Certified AI Practitioner AIF-C01 practice exam questions.
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How Will the AWS Certified AI Practitioner AIF-C01 Credential Help My Career?
Professionals in various roles, such as sales, marketing, and product management, will benefit significantly from this certification. Building skills through training and validating knowledge through certifications like AWS Certified AI Practitioner can lead to better job performance and career advancement.
According to a November 2023 conducted by AWS, employers are willing to pay:
- 43% more for AI-skilled workers in sales and marketing,
- 42% more for those in finance,
- 41% more for business operations,
- 47% more for IT professionals.
What AWS Certification Should I Earn Next?
- Cloud Careers: AWS Certified Solutions Architect – Associate SAA-C03.
- Data, AI, and ML Careers:
- AWS Certified Data Engineer – Associate DEA-C01
- AWS Certified Machine Learning Engineer – Associate MLA-C01.
Don’t miss this opportunity to advance your career with the AWS Certified AI Practitioner AIF-C01!
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Written by: Lois Dar Juan
