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AIF-C01 Detailed Study Plan, AIF-C01 Valid Test Notes

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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	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 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 (Q165-Q170):

NEW QUESTION # 165
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

A. Feature engineering
B. Exploratory data analysis
C. Hyperparameter tuning
D. Data pre-processing

Answer: B

Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

NEW QUESTION # 166
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

A. Observer bias
B. Confirmation bias
C. Measurement bias
D. Sampling bias

Answer: D

NEW QUESTION # 167
A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?

A. Create medication review summaries by using Amazon Bedrock large language models (LLMs).
B. Create medication review summaries by using Amazon Rekognition.
C. Create a classification model that categorizes medications into different groups by using Amazon SageMaker.
D. Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.

Answer: A

Explanation:
Amazon Bedrock provides large language models (LLMs) that are optimized for natural language understanding and text summarization tasks, making it the best choice for creating concise summaries of user reviews. Time-series forecasting, classification, and image analysis (Rekognition) are not suitable for summarizing textual data. Reference: AWS Bedrock Documentation.

NEW QUESTION # 168
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows
90% recall on training data and 40% recall on unseen testing data.
Which conclusion can the company draw from these results?

A. The model has insufficient testing data.
B. The model is overfitting on the training data.
C. The model has insufficient training data.
D. The model is underfitting on the training data.

Answer: B

Explanation:
The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Explanation:
* Option A: The model is overfitting on the training data.This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.
* Option B: The model is underfitting on the training data.Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.
* Option C: The model has insufficient training data.Insufficient training data could lead to poor performance, but the high recall on trainingdata (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.
* Option D: The model has insufficient testing data.Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.
References:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com
/sagemaker/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting and Underfitting (https://aws.amazon.com/machine-learning
/)

NEW QUESTION # 169
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?

A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
B. Data summarization by using Amazon QuickSight
C. Image recognition by using Amazon Rekognition
D. Data augmentation by using an Amazon Bedrock knowledge base

Answer: A

Explanation:
Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.
* Amazon SageMaker Ground Truth Plus:
* It allows for the creation of high-quality training datasets with human oversight, which minimizes errors in labeling and increases accuracy.
* Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated images for protective eyewear meet high-quality standards.
* Why Option A is Correct:
* High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in annotations, ensuring high-quality data.
* Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is especially important in use cases like generating precise images for protective eyewear.
* Why Other Options are Incorrect:
* B. Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running foundation models.
* C. Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing annotation errors.
* D. Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation tasks.
Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.

NEW QUESTION # 170
......

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Ashley Davis Ashley Davis

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AIF-C01 Detailed Study Plan, AIF-C01 Valid Test Notes

Amazon AIF-C01 Exam Syllabus Topics:

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Amazon AWS Certified AI Practitioner Sample Questions (Q165-Q170):

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Ashley Davis Ashley Davis

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AIF-C01 Detailed Study Plan, AIF-C01 Valid Test Notes

Amazon AIF-C01 Exam Syllabus Topics:

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Amazon AWS Certified AI Practitioner Sample Questions (Q165-Q170):

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