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Free PDF 2025 Amazon AIF-C01: First-grade AWS Certified AI Practitioner Actual Test

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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 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 (Q117-Q122):

NEW QUESTION # 117
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. Create effective prompts that provide clear instructions and context to guide the model's generation.
B. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
C. Select a large, diverse dataset to pre-train a new generative model.
D. Increase the model's complexity by adding more layers to the model's architecture.

Answer: A

Explanation:
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.
* Effective Prompt Engineering:
* Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.
* By providing explicit instructions in the prompts, the company can guide the AI to generate content that matches the brand's voice and messaging.
* Why Option C is Correct:
* Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the model's response through the prompt.
* Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resource-efficient.
* Why Other Options are Incorrect:
* A. Optimize the model's architecture and hyperparameters: Improves model performance but does not specifically address alignment with brand voice.
* B. Increase model complexity: Adding more layers may not directly help with content alignment.
* D. Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content alignment.

NEW QUESTION # 118
Which AWS feature records details about ML instance data for governance and reporting?

A. Amazon SageMaker Debugger
B. Amazon SageMaker JumpStart
C. Amazon SageMaker Model Monitor
D. Amazon SageMaker Model Cards

Answer: D

Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

NEW QUESTION # 119
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
B. Detect imbalances or disparities in the data.
C. Ensure that the model's inference time is within the accepted limits.
D. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
E. Ensure that the model runs frequently.

Answer: B,D

Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
* Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
* Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions.
Transparency is a crucial aspect of responsible AI.
* Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
* Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
* Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner References:
* Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
* Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

NEW QUESTION # 120
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. Register the model with the Amazon SageMaker Model Registry.
B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C. Purchase Provisioned Throughput for the custom model.
D. Grant access to the custom model in Amazon Bedrock.

Answer: B

NEW QUESTION # 121
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
B. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Answer: D

Explanation:
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
* Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
* Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
* Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
* Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner References:
* Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

NEW QUESTION # 122
......

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