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| 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.
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| Topic 2 | - 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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| 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.
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| Topic 4 | - 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.
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| 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 AIF-C01 Questions [2026]
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Amazon AWS Certified AI Practitioner Sample Questions (Q148-Q153):
NEW QUESTION # 148
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:
Explanation:

NEW QUESTION # 149
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?
- A. Amazon SageMaker Model Cards
- B. Amazon SageMaker Data Wrangler
- C. Amazon SageMaker Clarify
- D. Amazon SageMaker Feature Store
Answer: D
NEW QUESTION # 150
What does an F1 score measure in the context of foundation model (FM) performance?
- A. Financial cost of operating the model.
- B. Model speed in generating responses.
- C. Model precision and recall.
- D. Energy efficiency of the model's computations.
Answer: C
Explanation:
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.
NEW QUESTION # 151
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
- A. Directional stimulus prompting
- B. Complexity-based prompting
- C. Few-shot prompting
- D. Zero-shot prompting
Answer: A
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
NEW QUESTION # 152
A financial company is training a generative AI model to predict outcomes of loan applications. The training dataset is small. The dataset categorizes loan applicants as "younger-aged," "middle-aged," or "older-aged." Most individuals in the dataset are characterized as "middle-aged." The company removes the age range feature from the training dataset.
Which model behavior will likely happen as a result of this change to the dataset?
- A. The model will require less training data.
- B. The model will accurately predict outcomes for all ages.
- C. The model will inaccurately predict outcomes for younger and older age groups.
- D. The model will predict accurate outcomes for only younger age groups.
Answer: A
NEW QUESTION # 153
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