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100% Pass 2025 Amazon AIF-C01: Updated AWS Certified AI Practitioner Valid Dumps Demo

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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

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 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 (Q173-Q178):

NEW QUESTION # 173
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?

A. Increase the temperature parameter.
B. Decrease the epochs.
C. Increase the epochs.
D. Decrease the batch size.

Answer: C

Explanation:
Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.
Option B (Correct): "Increase the epochs": This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.
Option A: "Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.
Option C: "Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.
Option D: "Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.
AWS AI Practitioner Reference:
Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

NEW QUESTION # 174
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?

A. Amazon Kendra
B. Amazon Translate
C. Amazon Polly
D. Amazon Transcribe

Answer: B

Explanation:
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
Reference:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com/translate/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)

NEW QUESTION # 175
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?

A. Fine-tune the FM.
B. Retrain the FM.
C. Train a new FM.
D. Use prompt engineering.

Answer: D

Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost- effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
* Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
* Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
* Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost- effective.
* Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
References:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com
/bedrock/latest/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)

NEW QUESTION # 176
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. Transcribe call recordings by using Amazon Transcribe.
B. Extract information from call recordings by using Amazon SageMaker Model Monitor.
C. Create classification labels by using Amazon Comprehend.
D. Build a conversational chatbot by using Amazon Lex.

Answer: A

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 # 177
A company's large language model (LLM) is experiencing hallucinations.
How can the company decrease hallucinations?

A. Set up Agents for Amazon Bedrock to supervise the model training.
B. Decrease the temperature inference parameter for the model.
C. Use a foundation model (FM) that is trained to not hallucinate.
D. Use data pre-processing and remove any data that causes hallucinations.

Answer: B

Explanation:
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations.
Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest/devguide/)

NEW QUESTION # 178
......

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