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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 2	Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 3	Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 4	Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 5	Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.

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Amazon AWS Certified AI Practitioner Sample Questions (Q27-Q32):

NEW QUESTION # 27
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

A. Use transfer learning.
B. Increase the number of epochs.
C. Use unsupervised learning.
D. Decrease the number of epochs.

Answer: A

NEW QUESTION # 28
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?

A. Batch transform
B. Real-time inference
C. Serverless inference
D. Asynchronous inference

Answer: B

Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 29
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Adjust the temperature parameter of the model.
B. Avoid overfitting on the training data.
C. Modify the training data to mitigate bias.
D. Include fairness metrics for model evaluation.
E. Apply prompt engineering techniques.

Answer: C,D

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 30
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

A. Supervised learning.
B. Unsupervised learning.
C. Active learning.
D. Reinforcement learning.

Answer: A

Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.

NEW QUESTION # 31
A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

A. Privacy and security.
B. Transparency.
C. Fairness.
D. Explainability.

Answer: C

Explanation:
Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. References: AWS Responsible AI Framework.

NEW QUESTION # 32
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Three Easy-to-Use Formats of ExamPrepAway AIF-C01 Exam

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Amazon AWS Certified AI Practitioner Sample Questions (Q27-Q32):

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