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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	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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Amazon AWS Certified AI Practitioner Sample Questions (Q81-Q86):

NEW QUESTION # 81
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?

A. Ensure that the S3 data does not contain sensitive information.
B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
D. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.

Answer: D

NEW QUESTION # 82
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 Clarify
C. Amazon SageMaker Feature Store
D. Amazon SageMaker Data Wrangler

Answer: C

Explanation:
Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.
* Amazon SageMaker Feature Store:
* A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.
* It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.
* Why Option A is Correct:
* Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams.
* Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.
* Why Other Options are Incorrect:
* B. SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store.
* C. SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams.
* D. SageMaker Model Cards: Provide model documentation, not feature management.

NEW QUESTION # 83
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

A. F1 score
B. Mean squared error (MSE)
C. Time used to train the model
D. R-squared

Answer: A

Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 84
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 # 85
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

A. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
B. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
D. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.

Answer: A

NEW QUESTION # 86
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TRUSTWORTHY NEW AIF-C01 TEST PATTERN | EASY TO STUDY AND PASS EXAM AT FIRST ATTEMPT & EFFECTIVE AIF-C01: AWS CERTIFIED AI PRACTITIONER

Trustworthy New AIF-C01 Test Pattern | Easy To Study and Pass Exam at first attempt & Effective AIF-C01: AWS Certified AI Practitioner