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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.
Topic 5	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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q18-Q23):

NEW QUESTION # 18
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?

A. Multi-modal generation model
B. Text embedding model
C. Image generation model
D. Multi-modal embedding model

Answer: D

NEW QUESTION # 19
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

A. Define a higher number for the temperature parameter.
B. Set a low limit on the number of tokens the FM can produce.
C. Use batch inferencing to process detailed responses.
D. Experiment and refine the prompt until the FM produces the desired responses.

Answer: D

Explanation:
Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.
* Prompt Engineering:
* Adjusting and refining the prompt allows for better control over the FM's outputs, ensuring they align with the desired tone and style.
* This iterative process involves testing different prompts and modifying them based on the model's responses to achieve the desired outcome.
* Why Option C is Correct:
* Directly Influences Output Quality: Allows for fine-tuning of the model's responses to match the company's tone.
* Cost-Effective: Does not require modifying the model itself, only the inputs to it.
* Why Other Options are Incorrect:
* A. Low limit on tokens: Limits response length but not the adherence to company tone.
* B. Batch inferencing: Relates to processing multiple inputs, not controlling response tone.
* D. Higher temperature: Increases randomness in responses, which could deviate from the desired tone.

NEW QUESTION # 20
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. Increase the number of epochs.
B. Decrease the number of epochs.
C. Use unsupervised learning.
D. Use transfer learning.

Answer: D

Explanation:
Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.

NEW QUESTION # 21
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?

A. Track the model changes by using Amazon Fraud Detector.
B. Track the model changes by using Amazon Comprehend.
C. Track the model changes by using Amazon SageMaker Model Cards.
D. Track the model changes by using Git.

Answer: C

NEW QUESTION # 22
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. Avoid using LLMs that are not listed in Amazon SageMaker.
B. Create a prompt template that teaches the LLM to detect attack patterns.
C. Decrease the number of input tokens on invocations of the LLM.
D. Increase the temperature parameter on invocation requests to the LLM.

Answer: B

Explanation:
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.
* Prompt Templates for Security:
* A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
* This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
* Why Option A is Correct:
* Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
* Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
* Why Other Options are Incorrect:
* B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
* C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
* D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.

NEW QUESTION # 23
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AIF-C01 DUMPS FOR VCETORRENT - BEST

AIF-C01 Dumps For VCETorrent - Best