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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Exam Price: | 100 USD |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| Exam Duration: | 90 minutes |
| Passing Score: | 700 (scaled score 100–1000) |
| Available Languages: | Japanese, Simplified Chinese, English, Traditional Chinese, Korean |
| Exam Format: | Multiple choice, Ordering, Multiple response |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
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NEW QUESTION # 191
A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?
Answer: B
Explanation:
Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.
* D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.
* A (reducing data size) could worsen bias by removing potentially useful diverse data.
* B (consistency with historical results) might reinforce existing biases.
* C (separate models) is not scalable and can introduce other fairness issues.
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
NEW QUESTION # 192
A company wants to use an ML model to analyze customer reviews on social media. The model must determine if each review has a neutral, positive, or negative sentiment.
Answer: D
Explanation:
The correct answer is D - Classification. In this scenario, the goal is to assign each social media review to one of three predefined categories: positive, negative, or neutral. According to AWS documentation, classification models are used when "inputs must be mapped to one label from a fixed set of possible labels." Sentiment analysis is one of the most common NLP classification tasks and is supported in Amazon Comprehend, Amazon SageMaker, and Amazon Bedrock. Open-ended generation produces free-form text and is not appropriate for categorical outputs. Summarization condenses long-form content, and machine translation converts text across languages. Only classification aligns with sentiment detection, enabling the model to learn sentiment cues such as emotional wording or tone markers. AWS highlights sentiment classification as a key use case for supervised learning with text data.
Referenced AWS Documentation:
* Amazon Comprehend Documentation - Sentiment Classification
* AWS ML Specialty Guide - Classification Algorithms
NEW QUESTION # 193
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: D
Explanation:
According to the AWS Certified AI Practitioner documentation, explainable AI (XAI) refers to methods and techniques that make the behavior and predictions of machine learning models more understandable and transparent to users and stakeholders. In financial use cases, especially when decisions such as credit limits are made, regulatory and ethical concerns demand transparency about how such decisions are reached.
Option B is correct because applying explainable AI techniques (such as SHAP, LIME, or Amazon SageMaker Clarify) allows organizations to provide customers with clear insights into which data points or factors contributed to the model's decision. This aligns with best practices for responsible AI as defined in the AWS documentation, which states:
"Explainable AI increases transparency and trust in machine learning applications by helping users and regulators understand the decision process behind model predictions." (Reference: AWS AI/ML Best Practices - Explainable AI, AWS AI Practitioner Exam Guide) Option A suggests switching to a rule-based system, which is not practical for complex problems addressed by generative AI and may reduce model performance.
Option C (just a UI) does not inherently provide transparency into the model's reasoning, unless paired with explainability techniques.
Option D (accuracy over transparency) does not address the company's requirement for transparency.
Reference:
AWS Certified AI Practitioner Exam Guide
Amazon SageMaker Clarify Documentation
NEW QUESTION # 194
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
Explanation:
The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
" Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels. " (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes
/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing " Select..., " " Supervised learning, " and " Unsupervised learning. " The correct selections are:
Binary classification: Supervised learning
Multi-class classification: Supervised learning
K-means clustering: Unsupervised learning
Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)
NEW QUESTION # 195
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code.
Which Amazon SageMaker feature meets this requirement?
Answer: C
Explanation:
The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
"Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience." This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B). SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C). SageMaker Model Monitor monitors model quality in production but doesn't build or train models.
D). SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide - AutoML and No-Code Tools Section AWS Machine Learning Blog: "Predict Employee Attrition with SageMaker Canvas"
NEW QUESTION # 196
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