Today I want to share an article about the world’s earliest AI certification: Amazon AWS Certified AI Practitioner (AIF-C01) Exam
Since OpenAI launched ChatGPT in 2022, how many people have felt incredible, and how many people dream of pursuing a career in AI!
As of late August 2024, AWS has provided AWS Certified AI Practitioner (AIF-C01)certification as a basic certification. Compared with the AWS Certified Machine Learning (MLS-C01)certification launched earlier by Amazon, this means that the threshold for learning AWS AI solutions is now higher than It used to be much lower.
What’s more, from now until February 15, 2025, you can take the exam again for free if necessary.
Specific steps:
- Register for the exam: Book your appointment for either the AWS Certified AI Practitioner or AWS Certified Cloud Practitioner and enter the code AWSRetake2025 at checkout.
- Prepare for the exam
- The first exam is due until February 15, 2025, inclusive.
- If you fail, register for a second attempt of the same exam before March 31, 2025 and your exam retake will automatically be free!
Register now for your exam with code: AWSRetake2025
AIF-C01 Exam Overview
To earn the certificate, you’ll need to work in several areas: Fundamentals of Artificial Intelligence and Machine Learning, Fundamentals of Generative AI, Application of Fundamental Models, Guidelines for Responsible AI, and Security, Compliance, and Governance of AI Solutions.
Fundamentals of Artificial Intelligence and Machine Learning This section makes up approximately 20% of the exam and primarily covers basic concepts. Fundamentals of Generative AI accounts for approximately 25% of the graded content on the exam and covers explaining specific generative AI concepts. The application of the basic model accounts for 28% of the total score. The basic model (FM) is the key point that must be understood, and it mainly assists in fine-tuning specific tasks.
The Guide to Responsible AI accounts for about 14% of the exam and covers questions about responsible AI. Security, compliance, and governance of AI solutions accounts for about 14% of the exam and covers all AWS solutions for AI solutions.
For a comprehensive and detailed understanding of the exam content, be sure to read the AWS Certified AI Practitioner Exam Guide.
AIF-C01 Exam online practice
| From | Number of exam questions (Free) | Total Questions (Instant updates) | Related |
| Leads4Pass | 15 Q&As | 87 Q&As | AWS Certified Foundational |
Question 1:
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
A. Create a prompt template that teaches the LLM to detect attack patterns.
B. Increase the temperature parameter on invocation requests to the LLM.
C. Avoid using LLMs that are not listed in Amazon SageMaker.
D. Decrease the number of input tokens on invocations of the LLM.
Correct Answer: A
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
Question 2:
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?
A. Configure AWS CloudTrail as the logs destination for the model.
B. Enable invocation logging in Amazon Bedrock.
C. Configure AWS Audit Manager as the logs destination for the model.
D. Configure model invocation logging in Amazon EventBridge.
Correct Answer: B
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data. Option B
(Correct): “Enable invocation logging in Amazon Bedrock”:This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
Option A:”Configure AWS CloudTrail” is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs. Option C:”Configure AWS Audit Manager” is incorrect as Audit Manager is used for
compliance reporting, not specific invocation logging for AI models. Option D:”Configure model invocation logging in Amazon EventBridge” is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI
model inputs and outputs.
AWS AI Practitioner
References:
Amazon Bedrock Logging Capabilities:AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.
Question 3:
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?
A. Decrease the batch size.
B. Increase the epochs.
C. Decrease the epochs.
D. Increase the temperature parameter.
Correct Answer: B
Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of
accuracy. Option B (Correct): “Increase the epochs”:This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.
Option A:”Decrease the batch size” is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.
Option C:”Decrease the epochs” is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy. Option D:”Increase the temperature parameter” is incorrect because temperature affects the
randomness of predictions, not model accuracy.
AWS AI Practitioner
References:
Model Training Best Practices on AWS:AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.
Question 4:
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company\’s data.
Which strategy will successfully fine-tune the model?
A. Provide labeled data with the prompt field and the completion field.
B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
C. Purchase Provisioned Throughput for Amazon Bedrock.
D. Train the model on journals and textbooks.
Correct Answer: A
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
Question 5:
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
A. Helps decrease the model\’s complexity
B. Improves model performance over time
C. Decreases the training time requirement
D. Optimizes model inference time
Correct Answer: B
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
Question 6:
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
A. Configure the security and compliance by using Amazon Inspector.
B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
C. Encrypt and secure training data by using Amazon Macie.
D. Gather more data. Use Amazon Rekognition to add custom labels to the data.
Correct Answer: B
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
Question 7:
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
A. Amazon Q in Amazon EC2
B. Amazon Q Developer
C. Amazon Q in Amazon QuickSight
D. Amazon Q in AWS Chatbot
Correct Answer: C
Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. “Amazon Q” is the natural language query
feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.
Option C (Correct): “Amazon Q in Amazon QuickSight”:This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate
graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top- selling products across various retail locations.
Option A, B, and D:These options are incorrect:
AWS AI Practitioner
References:
Amazon QuickSight Qis designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.
Business Intelligence (BI) on AWS:AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like
sales trends over time.
Question 8:
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
A. Data pre-processing
B. Feature engineering
C. Exploratory data analysis
D. Hyperparameter tuning
Correct Answer: C
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further
steps in the ML pipeline. Option C (Correct): “Exploratory data analysis”:This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A:”Data pre-processing” is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B:”Feature engineering” is incorrect because it involves creating new features from raw data, not analyzing the data\’s existing structure. Option D:”Hyperparameter tuning” is incorrect because it refers to optimizing model parameters,
not analyzing the data.
AWS AI Practitioner References:
Stages of the Machine Learning Pipeline:AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
Question 9:
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company\’s brand voice and messaging requirements.
Which solution meets these requirements?
A. Optimize the model\’s architecture and hyperparameters to improve the model\’s overall performance.
B. Increase the model\’s complexity by adding more layers to the model\’s architecture.
C. Create effective prompts that provide clear instructions and context to guide the model\’s generation.
D. Select a large, diverse dataset to pre-train a new generative model.
Correct Answer: C
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company\’s brand voice and messaging requirements.
Question 10:
A company needs to build its own large language model (LLM) based on only the company\’s private data. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
A. Amazon EC2 C series
B. Amazon EC2 G series
C. Amazon EC2 P series
D. Amazon EC2 Trn series
Correct Answer: D
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost- effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have
been developed to minimize environmental impact by maximizing energy efficiency. Option D (Correct): “Amazon EC2 Trn series”:This is the correct answer because the Trn series is purpose-built for training deep learning models with lower
energy consumption, which aligns with the company\’s concern about environmental effects.
Option A:”Amazon EC2 C series” is incorrect because it is intended for compute- intensive tasks but not specifically optimized for ML training with environmental considerations.
Option B:”Amazon EC2 G series” (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
Option C:”Amazon EC2 P series” is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner References:
AWS Trainium Overview:AWS promotes Trainium instances as their most energy- efficient and cost-effective solution for ML model training.
Question 11:
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
A. Embeddings
B. Tokens
C. Models
D. Binaries
Correct Answer: A
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects\’ semantic meanings in a form that AI and NLP models can easily understand. These representations help models
improve their understanding of textual information by representing concepts in a continuous vector space. Option A (Correct): “Embeddings”:This is the correct term, as embeddings provide a way for models to learn relationships between
different objects in their input space, improving their understanding and processing capabilities. Option B:”Tokens” are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
Option C:”Models” are the algorithms that use embeddings and other inputs, not the representations themselves.
Option D:”Binaries” refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
Understanding Embeddings in AI and NLP:AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
Question 12:
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?
A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C. Provide the new text passage to be classified without any additional context or examples.
D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
Correct Answer: A
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
Question 13:
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV\’s compliance reports become available.
Which AWS service can the company use to meet this requirement?
A. AWS Audit Manager
B. AWS Artifact
C. AWS Trusted Advisor
D. AWS Data Exchange
Correct Answer: D
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications,
when new datasets or compliance reports become available.
Option D (Correct): “AWS Data Exchange”:This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs\’ compliance reports are available.
Option A:”AWS Audit Manager” is incorrect because it focuses on assessing an organization\’s own compliance, not receiving third-party compliance reports. Option B:”AWS Artifact” is incorrect as it provides access to AWS\’s compliance
reports, not ISVs\’.
Option C:”AWS Trusted Advisor” is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner
References:
AWS Data Exchange Documentation:AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
Question 14:
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
A. Integration with Amazon S3 for object storage
B. Support for geospatial indexing and queries
C. Scalable index management and nearest neighbor search capability
D. Ability to perform real-time analysis on streaming data
Correct Answer: C
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where
vector representations (embeddings) of data are often used to capture semantic meaning. Scalable index management and nearest neighbor search capabilityare the core features enabling vector database functionalities in OpenSearch. The
service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
Scalable Index Management:OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized
way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
Nearest Neighbor Search Capability:OpenSearch Service\’s nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applicationslike product recommendation engines, where the system
needs to quickly find the most similar items based on a user\’s query or behavior.
AWS AI Practitioner References:
The other options do not directly relate to building vector database applications:
A. Integration with Amazon S3 for object storageis about storing data objects, not vector-based searching or indexing.
B. Support for geospatial indexing and queriesis related to location-based data, not vectors used in machine learning.
D. Ability to perform real-time analysis on streaming datarelates to analyzing incoming data streams, which is different from the vector search capabilities.
Question 15:
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
A. Purchase Provisioned Throughput for the custom model.
B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C. Register the model with the Amazon SageMaker Model Registry.
D. Grant access to the custom model in Amazon Bedrock.
Correct Answer: B
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or
other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock. Option B (Correct): “Deploy the custom model in an Amazon
SageMaker endpoint for real-time inference”:This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A:”Purchase Provisioned Throughput for the custom model” is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C:”Register the model with the Amazon SageMaker Model Registry” is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference. Option D:”Grant access
to the custom model in Amazon Bedrock” is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner
References:
Amazon SageMaker Endpoints:AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
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More resources
If you want to know more about all the practice materials related to Amazon AWS Certified AI Practitioner to help you prepare for the AIF-C01 exam, please download the complete exam materials: https://www.leads4pass.com/aif-c01.html.
All about the new AWS Certified AI Practitioner exam (AIF-C01): https://www.pluralsight.com/resources/blog/ai-and-data/new-aws-aif-c01-exam
AWS Certified AI Practitioner (AIF-C01) –youtube: https://www.youtube.com/watch?v=WZeZZ8_W-M4
AWS Certified AI Practitioner Exam – linkedin: https://www.linkedin.com/incareer/pulse/aws-certified-ai-practitioner-exam-aif-c01-study-path-jon-bonso-icfpc
Differences from before:
AIF-C01 Exam adds three exam question types:
Sequencing, where you are given a list of 3 to 5 responses to complete a specific task and you need to select the correct responses and put them in the correct order.
Match and you will get a list of answers that match 3 to 7 prompts.
Case studies, where you are asked two or more questions about a scenario. In this case, each question will be scored individually.
I personally have a small suggestion. If each AWS certification page adds a Microsoft sandbox function, then the questions will be very simple. You can experience the exam environment in advance, which greatly reduces the situation where candidates are not adapted to the exam question type.
Ending
Amazon AWS Certified AI Practitioner (AIF-C01) Exam is a basic AI certification and the earliest AI certification so far. Amazon gives candidates one more free opportunity. From the current point of view, it is very suitable for those who dream of pursuing an AI career. people.
Use the Amazon AWS Certified AI Practitioner certification guide provided above to comprehensively and meticulously practice the knowledge in various fields involved in the AIF-C01 exam provided by Leads4Pass. I believe you can pass this exam on the first try.