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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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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Amazon AWS Certified AI Practitioner Sample Questions (Q92-Q97):

NEW QUESTION # 92
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. 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. Ensure that the model's inference time is within the accepted limits.
B. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
C. Detect imbalances or disparities in the data.
D. Ensure that the model runs frequently.
E. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

Answer: B,C

NEW QUESTION # 93
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. Use batch inferencing to process detailed responses.
B. Define a higher number for the temperature parameter.
C. Set a low limit on the number of tokens the FM can produce.
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 # 94
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?

A. Correlation matrix
B. Confusion matrix
C. Mean squared error (MSE)
D. R2 score

Answer: B

Explanation:
A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.
* Confusion Matrix:
* It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.
* Provides detailed insights into the model's performance, including true positives, true negatives, false positives, and false negatives.
* Why Option A is Correct:
* Performance Measurement: Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.
* Comprehensive Evaluation: Allows for a thorough analysis of where the model is making errors and the types of errors being made.
* Why Other Options are Incorrect:
* B. Correlation matrix: Used to identify relationships between variables, not for evaluating classification performance.
* C. R2 score: Used for regression models, not classification.
* D. Mean squared error (MSE): Also a metric for regression, measuring the average of the squares of the errors.

NEW QUESTION # 95
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. Asynchronous inference
B. Real-time inference
C. Serverless inference
D. Batch transform

Answer: B

NEW QUESTION # 96
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?

A. Purchase Provisioned Throughput for Amazon Bedrock.
B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
C. Provide labeled data with the prompt field and the completion field.
D. Train the model on journals and textbooks.

Answer: C

Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
* Fine-Tuning Strategy:
* To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
* This allows the model to learn the desired behavior or response style based on the provided examples.
* Why Option A is Correct:
* Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
* Customization: Ensures that the model is fine-tuned to the specific requirements of the company' s data and desired outputs.
* Why Other Options are Incorrect:
* B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
* C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
* D. Train on journals and textbooks: Lacks the specific format and labeling required for fine- tuning.

NEW QUESTION # 97
......

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Valid AIF-C01 Test Registration, AIF-C01 Trusted Exam Resource

Amazon AIF-C01 Exam Syllabus Topics:

Valid AIF-C01 Test Registration - 100% First-grade Questions Pool

Amazon AWS Certified AI Practitioner Sample Questions (Q92-Q97):