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Amazon AIF-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Applications of Foundation Models	- Use cases for generative AI in business - Content generation and summarization - AI-powered assistants and automation
Topic 2: Responsible AI and Security	- AI ethics and responsible use - Bias, fairness, and explainability - Security, privacy, and governance in AI systems
Topic 3: Fundamentals of Artificial Intelligence and Machine Learning	- Common ML workflows and lifecycle - Data fundamentals for AI/ML - Core AI and ML concepts
Topic 4: Fundamentals of Generative AI	- AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts - Foundation models and prompt engineering basics

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Amazon AWS Certified AI Practitioner Sample Questions (Q289-Q294):

NEW QUESTION # 289
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?

A. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
C. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
D. Ensure that the S3 data does not contain sensitive information.

Answer: C

NEW QUESTION # 290
Which task represents a practical use case to apply a regression model?

A. Use historical data to predict future temperatures in a specific city.
B. Suggest a genre of music for a listener from a list of genres.
C. Cluster movies based on movie ratings and viewers.
D. Create a picture that shows a specific object.

Answer: A

Explanation:
Comprehensive and Detailed
Regression predicts continuous numerical values (e.g., stock prices, temperatures).
A is classification (genre selection).
B is clustering.
D is generative AI/computer vision.
Reference:
AWS ML Glossary - Regression

NEW QUESTION # 291
A company is comparing two foundation models (FMs) for a customer service AI assistant. The company wants to evaluate the FMs based on helpfulness, correctness, and tone. The company needs an evaluation technique that is automated, repeatable, and does not require human reviewers.
Which evaluation technique will meet these requirements?

A. Retrieval Augmented Generation (RAG)
B. LLM-as-a-judge
C. String matching
D. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

Answer: B

Explanation:
AWS documentation describes LLM-as-a-judge as an automated evaluation technique where a large language model is used to assess the outputs of another model based on qualitative criteria such as helpfulness, correctness, tone, and alignment with expectations. This approach enables scalable and repeatable evaluations without requiring human reviewers.
In this scenario, the company needs to compare two foundation models across subjective dimensions that are difficult to measure using traditional metrics. LLM-as-a-judge allows the evaluator model to score or rank responses using predefined evaluation prompts and criteria, ensuring consistent and automated assessment.
The other options do not meet the requirements. String matching and ROUGE focus on lexical similarity and are unsuitable for evaluating tone or helpfulness in customer service interactions. Retrieval Augmented Generation is an architectural pattern, not an evaluation technique.
AWS highlights LLM-as-a-judge as a practical approach for automated qualitative evaluation of generative AI outputs, making it the correct choice.

NEW QUESTION # 292
A retail company has deployed an ML model to predict whether customers will purchase a product. The dataset is highly imbalanced. Only 5% of customers make purchases. The model shows 95% accuracy.
However, the company reports that the model rarely identifies actual buyers.
Which metric should the company use instead to evaluate the model's performance?

A. Training time for each epoch
B. Cost for each inference request
C. Overall accuracy percentage
D. F1 score

Answer: D

Explanation:
The verified answer is B. F1 score. This is a classic imbalanced classification problem. Only 5% of customers make purchases, so a model can achieve 95% accuracy by predicting "will not purchase" for nearly everyone.
That looks good numerically but fails the business purpose because the model rarely identifies actual buyers.
AWS documentation describes F1 as a balanced measure that takes class balance into account and defines it as the harmonic mean of precision and recall.
AWS machine learning documentation also explains that F1 is commonly used for classification tasks and that a high F1 score indicates the model performs well for both precision and recall. If precision or recall is weak, the F1 score drops, which helps expose problems that accuracy hides.
Option A is incorrect because overall accuracy is already misleading in the scenario. The model's 95% accuracy is probably caused by the majority class: customers who do not buy. Accuracy does not show whether the model identifies the minority class, which is the actual buyer group.
Option C is incorrect because training time for each epoch measures training efficiency, not prediction quality. A model can train quickly and still fail to identify buyers.
Option D is incorrect because cost per inference request is an operational cost metric. It does not evaluate whether the model correctly identifies purchasing customers.
Because the dataset is imbalanced and the model misses the minority class, the company should use F1 score instead of accuracy.

NEW QUESTION # 293
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

Answer:

Explanation:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 294
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