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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

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 3

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 4

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 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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AIF-C01 Certification Exam Cost - Valid AIF-C01 Exam Answers

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Amazon AWS Certified AI Practitioner Sample Questions (Q124-Q129):

NEW QUESTION # 124
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. Use unsupervised learning.
C. Increase the number of epochs.
D. Decrease the number of epochs.

Answer: A

NEW QUESTION # 125
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 Comprehend.
B. Track the model changes by using Amazon SageMaker Model Cards.
C. Track the model changes by using Git.
D. Track the model changes by using Amazon Fraud Detector.

Answer: B

NEW QUESTION # 126
A company is building a contact center application and wants to gain insights from customer conversations.
The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?

A. Build a conversational chatbot by using Amazon Lex.
B. Transcribe call recordings by using Amazon Transcribe.
C. Create classification labels by using Amazon Comprehend.
D. Extract information from call recordings by using Amazon SageMaker Model Monitor.

Answer: B

Explanation:
Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.
* Amazon Transcribe:
* It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.
* After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.
* Why Option B is Correct:
* Conversion to Text: Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.
* Enables Further Analysis: Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.
* Why Other Options are Incorrect:
* A. Amazon Lex: Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.
* C. Amazon SageMaker Model Monitor: Monitors ML models for bias and data drift, not for audio analysis.
* D. Amazon Comprehend: Can analyze text but cannot transcribe audio; it would be used after transcription is completed.

NEW QUESTION # 127
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

A. Temperature value
B. Total training time
C. Number of tokens consumed
D. Amount of data used to train the LLM

Answer: C

NEW QUESTION # 128
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. Avoid overfitting on the training data.
B. Modify the training data to mitigate bias.
C. Adjust the temperature parameter of the model.
D. Include fairness metrics for model evaluation.
E. Apply prompt engineering techniques.

Answer: B,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 # 129
......

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